Etika bisnis

November 26, 2010

ETIKA BISNIS :
Pemikiran atau refleksi tentang moralitas dalam ekonomi dan bisnis. Moralitas berarti aspek baik atau buruk, terpuji atau tercela, dan karenanya diperbolehkan atau tidak, dari perilaku manusia. Moralitas selalu berkaitan dengan apa yang dilakukan manusia, dan kegiatan ekonomis merupakan suatu bidang perilaku manusia yang penting.
Apa yang diharapkan dan mengapa kita mempelajari Etika Bisnis?
Menurut K. Bertens, ada 3 tujuan yang ingin dicapai, yaitu :
1. Menanamkan atau meningkakan kesadaran akan adanya demensi etis dalam bisnis. Menanamkan, jika sebelumnya kesadaran itu tidak ada, meningkatkan bila kesadaran itu sudah ada, tapi masih lemah dan ragu. Orang yang mendalami etika bisnis diharapkan memperoleh keyakinan bahwa etika merupakan segi nyata dari kegiatan ekonomis yang perlu diberikan perhatian serius.
2. Memperkenalkan argumentasi moral khususnya dibidang ekonomi dan bisnis, serta membantu pebisnis/calon pebisnis dalam menyusun argumentasi moral yang tepat.
Dalam etika sebagai ilmu, bukan Baja penting adanya norma-norma moral, tidak kalah penting adalah alasan bagi berlakunya norma-norma itu. Melalui studi etika diharapkan pelaku bisnis akan sanggup menemukan fundamental rasional untuk aspek moral yang menyangkut ekonomi dan bisnis.
3. Membantu pebisnis/calon pebisnis, untuk menentukan sikap moral yang tepat didalam profesinya (kelak).
Hal ketiga ini memunculkan pertanyaan, apakah studi etika ini menjamin seseorang akan menjadi etis juga? Jawabnya, sekurang-kurangnya meliputi dua sisi berikut, yaitu disatu pihak, harus dikatakan : etika mengikat tetapi tidak memaksa. Disisi lain, studi dan pengajaran tentang etika bisnis boleh diharapkan juga mempunyai dampak atas tingkah laku pebisnis. Bila studi etika telah membuka mata, konsekuensi logisnya adalah pebisnis bertingkah laku menurut yang diakui sebagai hal yang benar.
Tiga aspek pokok dari bisnis yaitu : dari sudut pandang ekonomi, hukum dan etika.
1. Sudut pandang ekonomis.
Bisnis adalah kegiatan ekonomis. Yang terjadi disini adalah adanya interaksi antara produsen/perusahaan dengan pekerja, produsen dengan konsumen, produsen dengan produsen dalam sebuah organisasi. Kegiatan antar manusia ini adalah bertujuan untuk mencari untung oleh karena itu menjadi kegiatan ekonomis. Pencarian keuntungan dalam bisnis tidak bersifat sepihak, tetapi dilakukan melalui interaksi yang melibatkan berbagai pihak.
Dari sudut pandang ekonomis, good business adalah bisnis yang bukan saja menguntungkan, tetapi juga bisnis yang berkualitas etis.
2. Sudut pandang moral.
Dalam bisnis, berorientasi pada profit, adalah sangat wajar, akan tetapi jangan keuntungan yang diperoleh tersebut justru merugikan pihak lain. Tidak semua yang bisa kita lakukan boleh dilakukan juga. Kita harus menghormati kepentingan dan hak orang lain. Pantas diperhatikan, bahwa dengan itu kita sendiri tidak dirugikan, karena menghormati kepentingan dan hak orang lain itu juga perlu dilakukan demi kepentingan bisnis kita sendiri.
3. Sudut pandang Hukum.
Bisa dipastikan bahwa kegiatan bisnis juga terikat dengan “Hukum” Hukum Dagang atau Hukum Bisnis, yang merupakan cabang penting dari ilmu hukum modern. Dan dalam praktek hukum banyak masalah timbul dalam hubungan bisnis, pada taraf nasional maupun international. Seperti etika, hukum juga merupakan sudut pandang normatif, karena menetapkan apa yang harus dilakukan atau tidak boleh dilakukan. Dari segi norma, hukum lebih jelas dan pasti daripada etika, karena peraturan hukum dituliskan hitam atas putih dan ada sanksi tertentu bila terjadi pelanggaran. Bahkan pada zaman kekaisaran Roma, ada pepatah terkenal : “Quid leges sine moribus” yang artinya : “apa artinya undang-undang kalau tidak disertai moralitas ”
Lalu apa tolok ukur bahwa bisnis itu baik menurut tiga sudut pandang tadi?
Untuk sudut pandang ekonomis, jawaban pertanyaan ini lebih mudah, yaitu bila bisnis memberikan profit, dan hal ini akan jelas terbaca pada laporan rugi/laba perusahaan di akhir tahun. Dari sudut pandang hukum pun jelas, bahwa bisnis yang baik adalah yang diperbolehkan oleh sistem hukum yang berlaku. (penyelundupan adalah bisnis yang tidak baik).
Yang lebih sulit jawabnya adalah bila bisnis dilihat dari sudut pandang moral. Apa yang menjadi tolok ukur untuk menentukan baik buruknya suatu perbuatan bisnis.
Dari sudut pandang moral, setidaknya ada 3 tolok ukur yaitu : nurani, Kaidah Emas, penilaian umum.
1. Hati nurani:
Suatu perbuatan adalah baik, bila dilakukan susuai dengan hati nuraninya, dan perbuatan lain buruk bila dilakukan berlawanan dengan hati nuraninya. Kalau kita mengambil keputusan moral berdasarkan hati nurani, keputusan yang diambil “dihadapan Tuhan” dan kita sadar dengan tindakan tersebut memenuhi kehendak Tuhan.
2. Kaidah Emas :
Cara lebih obyektif untuk menilai baik buruknya perilaku moral adalah mengukurnya dengan Kaidah Emas (positif), yang berbunyi : “Hendaklah memperlakukan orang lain sebagaimana Anda sendiri ingin diperlakukan” Kenapa begitu? Tentunya kita menginginkan diperlakukan dengan baik. Kalau begitu yang saya akan berperilaku dengan baik (dari sudut pandang moral). Rumusan Kaidah Emas secara negatif : “Jangan perlakukan orang lain, apa yang Anda sendiri tidak ingin akan dilakukan terhadap diri Anda” Saya kurang konsisten dalam tingkah laku saya, bila saya melakukan sesuatu terhadap orang lain, yang saya tidak mau akan dilakukan terhadap diri saya. Kalau begitu, saya berperilaku dengan cara tidak baik (dari sudut pandang moral).
3. Penilaian Umum :
Cara ketiga dan barangkali paling ampuh untuk menentukan baik buruknya suatu perbuatan atau perilaku adalah menyerahkan kepada masyarakat umum untuk menilai. Cara ini bisa disebut juga audit sosial. Sebagaimana melalui audit dalam arti biasa sehat tidaknya keadaan finansial suatu perusahaan dipastikan, demikian juga kualitas etis suatu perbuatan ditentukan oleh penilaian masyarakat umum.

Apa itu etika bisnis?
Kata “etika” dan “etis” tidak selalu dipakai dalam arti yang sama dan karena itu pula “etika bisnis” bisa berbeda artinya.
Etika sebagai praksis berarti : nilai-nilai dan norma-norma moral sejauh dipraktekkan atau justru tidak dipraktekkan, walaupun seharusnya dipraktekkan. Sedangkanetis, merupakansifat daritindakan yang sesuai dengan etika.
Peranan Etika dalam Bisnis :
Menurut Richard De George, bila perusahaan ingin sukses/berhasil memerlukan 3 hal pokok yaitu :
1. Produk yang baik
2. Managemen yang baik
3. Memiliki Etika
Selama perusahaan memiliki produk yang berkualitas dan berguna untuk masyarakat disamping itu dikelola dengan manajemen yang tepat dibidang produksi, finansial, sumberdaya manusia dan lain-lain tetapi tidak mempunyai etika, maka kekurangan ini cepat atau lambat akan menjadi batu sandungan bagi perusahaan tsb. Bisnis merupakan suatu unsur mutlak perlu dalam masyarakat modern. Tetapi kalau merupakan fenomena sosial yang begitu hakiki, bisnis tidak dapat dilepaskan dari aturan-aturan main yang selalu harus diterima dalam pergaulan sosial, termasuk juga aturan-aturan moral.
Mengapa bisnis harus berlaku etis ?
Tekanan kalimat ini ada pada kata “harus”. Dengan kata lain, mengapa bisnis tidak bebas untuk berlaku etis atau tidak? Tentu saja secara faktual, telah berulang kali terjadi hal-hal yang tidak etis dalam kegiatan bisnis, dan hal ini tidak perlu disangkal, tetapi juga tidak perlu menjadi fokus perhatian kita. Pertanyaannya bukan tentang kenyataan faktual, melainkan tentang normativitas : seharusnya bagaimana dan apa yang menjadi dasar untuk keharusan itu. Mengapa bisnis harus berlaku etis, sebetulnya sama dengan bertanya mengapa manusia pada umumnya harus berlaku etis. Bisnis disini hanya merupakan suatu bidang khusus dari kondisi manusia yang umum.
Jawabannya ada tiga yaitu :
• Tuhan melalui agama/kepercayaan yang dianut, diharapkan setiap pebisnis akan dibimbing oleh iman kepercayaannya, dan menjadi tugas agama mengajak para pemeluknya untuk tetap berpegang pada motivasi moral.
• Kontrak Sosial, umat manusia seolah-olah pernah mengadakan kontrak yang mewajibkan setiap anggotanya untuk berpegang pada norma-norma moral, dan kontrak ini mengikat kita sebagai manusia, sehingga tidak ada seorangpun yang bisa melepaskan diri daripadanya.
• Keutamaan, Menurut Plato dan Aristoteles, manusia harus melakukan yang baik, justru karena hal itu baik. Yang baik mempunyai nilai intrinsik, artinya, yang baik adalah baik karena dirinya sendiri. Keutamaan sebagai disposisi tetap untuk melakukan yang baik, adalah penyempurnaan tertinggi dari kodrat manusia. Manusia yang berlaku etis adalah baik begitu saja, baik secara menyeluruh, bukan menurut aspek tertentu saja.

GCG (good corporate governance)

November 26, 2010

Pengertian dan Prinsip Dasar
Good Corporate Governance (GCG)

‘The proper governance of companies will become as crucial to the world economies as the proper governing of countries.’
(James D. Wolfensohn, President of the World Bank, c. 1999)

Sulit dipungkiri, selama sepuluh tahun terakhir ini, istilah Good Corporate Governance (GCG) kian populer. Tak hanya populer, tetapi istilah tersebut juga ditempatkan di posisi terhormat. Hal itu, setidaknya terwujud dalam dua keyakinan. Pertama, GCG merupakan salah satu kunci sukses perusahaan untuk tumbuh dan menguntungkan dalam jangka panjang, sekaligus memenangkan persaingan bisnis global – terutama bagi perusahaan yang telah mampu berkembang sekaligus menjadi terbuka.
Kedua, krisis ekonomi dunia, di kawasan Asia dan Amerika Latin yang diyakini muncul karena kegagalan penerapan GCG. Di antaranya, Sistem Regulatory yang payah, Standar Akuntansi dan Audit yang tidak konsisten, praktek perbankan yang lemah, serta pandangan Board of Directors (BOD) yang kurang peduli terhadap hak-hak pemegang saham minoritas.
Berdasarkan keyakinan-keyakinan di atas itulah maka tidak mengherankan jika selama dasawarsa 1990-an, tuntutan terhadap penerapan GCG secara konsisten dan komprehensif datang secara beruntun. Mereka yang menyuarakan hal itu di antaranya adalah berbagai lembaga investasi baik domestik maupun mancanegara, termasuk institusi sekaliber World Bank, IMF, OECD, dan APEC. Dengan melontarkan beberapa prinsip umum dalam CG seperti fairness, transparency, accountability, stakeholder concern, dapat disimpulkan bahwa penerapan GCG diyakini akan menolong perusahaan dan perekonomian negara yang sedang tertimpa krisis bangkit menuju ke arah yang lebih sehat, maju, mampu bersaing, dikelola secara dinamis serta profesional. Ujungnya adalah daya saing yang tangguh, yang diikuti pulihnya kepercayaan investor.
Tentunya, lembaga-lembaga besar itu tak asal bicara. Namun, apa sebetulnya GCG itu sendiri? Apa prinsip-prinsip dasar yang dikandungnya? Lantas, apa manfaat menerapkan GCG?

Sangat jelas bahwa perhatian terhadap corporate governance belakangan ini terutama dipicu oleh skandal spektakuler perusahaan-perusahaan publik di Amerika dan Eropa, seperti Enron, Worldcom, Tyco, London & Commonwealth, Poly Peck, Maxwell, dan lain-lain.Cadbury Report (UK) dan Treadway Report (US) secara mendasar menyebutkan bahwa keruntuhan perusahaan-perusahaan publik tersebut dikarenakan oleh kegagalan strategi maupun praktik curang dari manajemen puncak yang berlangsung tanpa terdeteksi dalam waktu yang cukup lama karena lemahnya pengawasan yang independen oleh corporate boards.

Isu corporate governance itu sendiri muncul sejak diperkenalkannya pemisahan antara kepemilikan dan pengelolaan perusahaan (Tri Gunarsih, 2003). Namun istilah corporate governance itu sendiri secara eksplisit muncul pertama kali pada tahun 1984 dalam tulisan Robert I. Tricker. Di dalam bukunya, Tricker memandang corporate governance memiliki empat kegiatan utama sebagai berikut:

Direction: Formulating the strategic direction from the future of the enterprise in the long term;
Executive action: Involvement in crucial executive decisions;
Supervision: Monitoring and oversight of management performance, and
Accountability: Recognizing responsibilities to those making legitimate demand for accountability.

(Tricker, Robert I., 1984, Corporate Governance – Practices, Procedures, and Power in British Companies and Their Board of Directors, UK, Gower)

Teori-teori Terkait
Dua teori utama yang terkait dengan corporate governance adalah stewardship theory dan agency theory. Stewardship theory dibangun di atas asumsi filosofis mengenai sifat manusia yakni bahwa manusia pada hakekatnya dapat dipercaya, mampu bertindak dengan penuh tanggung jawab memiliki, integritas, dan kejujuran terhadap pihak lain. Inilah yang tersirat dalam hubungan fidusia yang dikehendaki para pemegang saham. Dengan kata lain, stewardship theory memandang manajemen sebagai dapat dipercaya untuk bertindak dengan sebaik-baiknya bagi kepentingan publik pada umumnya maupun shareholders pada khususnya.

Sementara itu, agency theory yang dikembangkan oleh Michael Johnson, seorang professor dari Harvard, memandang bahwa manajemen perusahaan sebagai ‘agents’ bagi para pemegang saham, akan bertindak dengan penuh kesadaran bagi kepentingannya sendiri, bukan sebagai pihak yang arif dan bijaksana serta adil terhadap pemegang saham sebagaimana diasumsikan dalam stewardship model. Bertentangan dengan stewardship theory, agency theory memandang bahwa manajemen tidak dapat dipercaya untuk bertindak dengan sebaik-baiknya bagi kepentingan publik pada umumnya maupun shareholders pada khususnya. Dengan demikian, “managers could not be trusted to do their job – which of course is to maximize shareholder value’ (Tricker, Opcit).

Dalam perkembangan selanjutnya, agency theory mendapat respons lebih luas karena dipandang lebih mencerminkan kenyataan yang ada. Berbagai pemikiran mengenai corporate governance berkembang dengan bertumpu pada agency theory di mana pengelolaan perusahaan harus diawasi dan dikendalikan untuk memastikan bahwa pengelolaan dilakukan dengan penuh kepatuhan kepada berbagai peraturan dan ketentuan yang berlaku. Upaya ini menimbulkan apa yang disebut sebagai agency costs, yang menurut teori ini harus dikeluarkan sedemikian rupa sehingga biaya untuk mengurangi kerugian yang timbul karena ketidakpatuhan setara dengan peningkatan biaya enforcement-nya.

‘Biaya’ yang harus dibayar tersebut, dalam konteks corporate governance, adalah biaya untuk:

“…control managerial ‘opportunism’ by having a board chair independent of the CEO and using incentives to bind CEO interests to those of shareholders (Jensen, M.C., and W.H. Meckling (1986), ‘Theory of the firm – managerial behaviour, agency costs and ownership structure, “ Journal of Financial Economics, No. 3, pp. 305-60).

Agency costs ini mencakup biaya untuk pengawasan oleh pemegang saham; biaya yang dikeluarkan oleh manajemen untuk menghasilkan laporan yang transparan, termasuk biaya audit yang independen dan pengendalian internal; serta biaya yang disebabkan karena menurunnya nilai kepemilikan pemegang saham sebagai bentuk ‘bonding expenditures’ yang diberikan kepada manajemen dalam bentuk opsi dan berbagai manfaat untuk tujuan menyelaraskan kepentingan manajemen dengan pemegang saham.

Meskipun demikian, potensi untuk munculnya agency problem tetap ada karena adanya pemisahan antara kepengurusan dengan kepemilikan perusahaan, khususnya di perusahaan-perusahaan publik.

Bagaimana perbandingan kegiatan antara corporate governance dan corporate management memperlihatkan bahwa corporate governance sangat terkait dengan aspek pengawasan dan akuntabilitas, sementara corporate management terkait dengan keputusan-keputusan dan pengendalian eksekutif serta manajemen operasional. Sementara itu, titik temu atau irisan antara keduanya dalam banyak hal terwujud dalam pengambilan keputusan-keputusan strategik perusahaan sebagaimana terlihat pada gambar berikut ini:

Definisi Good Corporate Governance (GCG)
Sebagai sebuah konsep, GCG ternyata tak memiliki definisi tunggal. Komite Cadburry, misalnya, pada tahun 1992 – melalui apa yang dikenal dengan sebutan Cadburry Report – mengeluarkan definisi tersendiri tentang GCG. Menurut Komite Cadburry, GCG adalah prinsip yang mengarahkan dan mengendalikan perusahaan agar mencapai keseimbangan antara kekuatan serta kewenangan perusahaan dalam memberikan pertanggungjawabannya kepada para shareholders khususnya, dan stakeholders pada umumnya. Tentu saja hal ini dimaksudkan pengaturan kewenangan Direktur, manajer, pemegang saham, dan pihak lain yang berhubungan dengan perkembangan perusahaan di lingkungan tertentu.
Center for European Policy Studies (CEPS), punya formula lain. GCG, papar pusat studi ini, merupakan seluruh sistem yang dibentuk mulai dari hak (right), proses, serta pengendalian, baik yang ada di dalam maupun di luar manajemen perusahaan. Sebagai catatan, hak di sini adalah hak seluruh stakeholders, bukan terbatas kepada shareholders saja. Hak adalah berbagai kekuatan yang dimiliki stakeholders secara individual untuk mempengaruhi manajemen. Proses, maksudnya adalah mekanisme dari hak-hak tersebut. Adapun pengendalian merupakan mekanisme yang memungkinkan stakeholders menerima informasi yang diperlukan seputar aneka kegiatan perusahaan.
Sejumlah negara juga mempunyai definisi tersendiri tentang GCG. Beberapa negara mendefinisikannya dengan pengertian yang agak mirip walaupun ada sedikit perbedaan istilah. Kelompok negara maju (OECD), umpamanya mendefinisikan GCG sebagai cara-cara manajemen perusahaan bertanggung jawab pada shareholder-nya. Para pengambil keputusan di perusahaan haruslah dapat dipertanggungjawabkan, dan keputusan tersebut mampu memberikan nilai tambah bagi shareholders lainnya. Karena itu fokus utama di sini terkait dengan proses pengambilan keputusan dari perusahaan yang mengandung nilai-nilai transparency, responsibility, accountability, dan tentu saja fairness.
Sementara itu, ADB (Asian Development Bank) menjelaskan bahwa GCG mengandung empat nilai utama yaitu: Accountability, Transparency, Predictability dan Participation. Pengertian lain datang dari Finance Committee on Corporate Governance Malaysia. Menurut lembaga tersebut GCG merupakan suatu proses serta struktur yang digunakan untuk mengarahkan sekaligus mengelola bisnis dan urusan perusahaan ke arah peningkatan pertumbuhan bisnis dan akuntabilitas perusahaan. Adapun tujuan akhirnya adalah menaikkan nilai saham dalam jangka panjang tetapi tetap memperhatikan berbagai kepentingan para stakeholder lainnya.
Lantas bagaimana dengan definisi GCG di Indonesia? Di tanah air, secara harfiah, governance kerap diterjemahkan sebagai “pengaturan.” Adapun dalam konteks GCG, governance sering juga disebut “tata pamong”, atau penadbiran – yang terakhir ini, bagi orang awam masih terdengar janggal di telinga. Maklum, istilah itu berasal dari Melayu. Namun tampaknya secara umum di kalangan pebisnis, istilah GCG diartikan tata kelola perusahaan, meskipun masih rancu dengan terminologi manajemen. Masih diperlukan kajian untuk mencari istilah yang tepat dalam bahasan Indonesia yang benar.
Kemudian, “GCG” ini didefinisikan sebagai suatu pola hubungan, sistem, dan proses yang digunakan oleh organ perusahaan (BOD, BOC, RUPS) guna memberikan nilai tambah kepada pemegang saham secara berkesinambungan dalam jangka panjang, dengan tetap memperhatikan kepentingan stakeholder lainnya, berlandaskan peraturan perundangan dan norma yang berlaku.
Dari definisi di atas dapat disimpulkan bahwa Good Corporate Governance merupakan:

1. Suatu struktur yang mengatur pola hubungan harmonis tentang peran dewan komisaris, Direksi, Pemegang Saham dan Para Stakeholder lainnya.
2. Suatu sistem pengecekan dan perimbangan kewenangan atas pengendalian perusahaan yang dapat membatasi munculnya dua peluang: pengelolaan yang salah dan penyalahgunaan aset perusahaan.
3. Suatu proses yang transparan atas penentuan tujuan perusahaan, pencapaian, berikut pengukuran kinerjanya.

Dari pengertian di atas pula, tampak beberapa aspek penting dari GCG yang perlu dipahami beragam kalangan di dunia bisnis, yakni;

 Adanya keseimbangan hubungan antara organ-organ perusahaan di antaranya Rapat Umum Pemegang Saham (RUPS), Komisaris, dan direksi. Keseimbangan ini mencakup hal-hal yang berkaitan dengan struktur kelembagaan dan mekanisme operasional ketiga organ perusahaan tersebut (keseimbangan internal)
 Adanya pemenuhan tanggung jawab perusahaan sebagai entitas bisnis dalam masyarakat kepada seluruh stakeholder. Tanggung jawab ini meliputi hal-hal yang terkait dengan pengaturan hubungan antara perusahaan dengan stakeholders (keseimbangan eksternal). Di antaranya, tanggung jawab pengelola/pengurus perusahaan, manajemen, pengawasan, serta pertanggungjawaban kepada para pemegang saham dan stakeholders lainnya.
 Adanya hak-hak pemegang saham untuk mendapat informasi yang tepat dan benar pada waktu yang diperlukan mengenai perusahaan. Kemudian hak berperan serta dalam pengambilan keputusan mengenai perkembangan strategis dan perubahan mendasar atas perusahaan serta ikut menikmati keuntungan yang diperoleh perusahaan dalam pertumbuhannya.
 Adanya perlakuan yang sama terhadap para pemegang saham, terutama pemegang saham minoritas dan pemegang saham asing melalui keterbukaan informasi yang material dan relevan serta melarang penyampaian informasi untuk pihak sendiri yang bisa menguntungkan orang dalam (insider information for insider trading).

Empat Prinsip Utama Corporate Governance
Setelah definisi serta aspek penting GCG terpaparkan di atas, maka berikut adalah prinsip yang dikandung dalam GCG. Di sini secara umum ada empat prinsip utama yaitu: fairness, transparency, accountability, dan responsibility.

1. Fairness (Kewajaran)
Secara sederhana kewajaran (fairness) bisa didefinisikan sebagai perlakuan yang adil dan setara di dalam memenuhi hak-hak stakeholder yang timbul berdasarkan perjanjian serta peraturan perundangan yang berlaku.
Fairness juga mencakup adanya kejelasan hak-hak pemodal, sistem hukum dan penegakan peraturan untuk melindungi hak-hak investor – khususnya pemegang saham minoritas – dari berbagai bentuk kecurangan. Bentuk kecurangan ini bisa berupa insider trading (transaksi yang melibatkan informasi orang dalam), fraud (penipuan), dilusi saham (nilai perusahaan berkurang), KKN, atau keputusan-keputusan yang dapat merugikan seperti pembelian kembali saham yang telah dikeluarkan, penerbitan saham baru, merger, akuisisi, atau pengambil-alihan perusahaan lain.
Biasanya, penyakit yang timbul dalam praktek pengelolaan perusahaan, berasal dari benturan kepentingan. Baik perbedaan kepentingan antara manajemen (Dewan Komisaris dan Direksi) dengan pemegang saham, maupun antara pemegang saham pengendali (pemegang saham pendiri, di Indonesia biasanya mayoritas) dengan pemegang saham minoritas (pada perusahaan publik biasanya pemegang saham publik). Di tengah situasi seperti ini, lewat prinsip fairness, ada beberapa manfaat yang diharapkan bisa dipetik. Apa saja manfaat itu?
Fairness diharapkan membuat seluruh aset perusahaan dikelola secara baik dan prudent (hati-hati), sehingga muncul perlindungan kepentingan pemegang saham secara fair (jujur dan adil). Fairness juga diharapkan memberi perlindungan kepada perusahaan terhadap praktek korporasi yang merugikan seperti disebutkan di atas. Pendek kata, fairness menjadi jiwa untuk memonitor dan menjamin perlakuan yang adil di antara beragam kepentingan dalam perusahaan.
Namun seperti halnya sebuah prinsip, fairness memerlukan syarat agar bisa diberlakukan secara efektif. Syarat itu berupa peraturan dan perundang-undangan yang jelas, tegas, konsisten dan dapat ditegakkan secara baik serta efektif. Hal ini dinilai penting karena akan menjadi penjamin adanya perlindungan atas hak-hak pemegang saham manapun, tanpa ada pengecualian. Peraturan perundang-undangan ini harus dirancang sedemikian rupa sehingga dapat menghindari penyalahgunaan lembaga peradilan (litigation abuse). Di antara (litigation abuse) ini adalah penyalahgunaan ketidakefisienan lembaga peradilan dalam mengambil keputusan sehingga pihak yang tidak beritikad baik mengulur-ngulur waktu kewajiban yang harus dibayarkannya atau bahkan dapat terbebas dari kewajiban yang harus dibayarkannya.

2. Transparency (Keterbukaan Informasi)
Transparansi bisa diartikan sebagai keterbukaan informasi, baik dalam proses pengambilan keputusan maupun dalam mengungkapkan informasi material dan relevan mengenai perusahaan.
Perbincangan prinsip ini sendiri sangatlah menarik. Pasalnya, isu yang sering mencuat adalah pertentangan dalam menjalankan prinsip ini. Semisal, adanya kekhawatiran perusahaan bahwa jika ia terlalu terbuka, maka strateginya dapat diketahui pesaing sehingga membahayakan kelangsungan usahanya. Wajarkah kekhawatiran seperti itu?
Menurut peraturan di pasar modal Indonesia, yang dimaksud informasi material dan relevan adalah informasi yang dapat mempengaruhi naik turunnya harga saham perusahaan tersebut, atau yang mempengaruhi secara signifikan risiko serta prospek usaha perusahaan yang bersangkutan. Mengingat definisi ini sangat normatif maka perlu ada penjelasan operasionalnya di tiap perusahaan. Karenanya, kekhawatiran di atas, sebetulnya tidak perlu muncul jika kita mampu menjabarkan kriteria informasi material secara spesifik bagi masing-masing perusahaan.
Dalam mewujudkan transparansi ini sendiri, perusahaan harus menyediakan informasi yang cukup, akurat, dan tepat waktu kepada berbagai pihak yang berkepentingan dengan perusahaan tersebut. Setiap perusahaan, diharapkan pula dapat mempublikasikan informasi keuangan serta informasi lainnya yang material dan berdampak signifikan pada kinerja perusahaan secara akurat dan tepat waktu. Selain itu, para investor harus dapat mengakses informasi penting perusahaan secara mudah pada saat diperlukan.
Ada banyak manfaat yang bisa dipetik dari penerapan prinsip ini. Salah satunya, stakeholder dapat mengetahui risiko yang mungkin terjadi dalam melakukan transaksi dengan perusahaan. Kemudian, karena adanya informasi kinerja perusahaan yang diungkap secara akurat, tepat waktu, jelas, konsisten, dan dapat diperbandingkan, maka dimungkinkan terjadinya efisiensi pasar. Selanjutnya, jika prinsip transparansi dilaksanakan dengan baik dan tepat, akan dimungkinkan terhindarnya benturan kepentingan (conflict of interest) berbagai pihak dalam manajemen.

3. Accountability (Dapat Dipertanggungjawabkan)
Akuntabilitas adalah kejelasan fungsi, struktur, sistem dan pertangungjawaban organ perusahaan sehingga pengelolaan perusahaan terlaksana secara efektif.
Masalah yang sering ditemukan di perusahaan-perusahaan Indonesia adalah mandulnya fungsi pengawasan Dewan Komisaris. Atau justru sebaliknya, Komisaris Utama mengambil peran berikut wewenang yang seharusnya dijalankan direksi. Padahal, diperlukan kejelasan tugas serta fungsi organ perusahaan agar tercipta suatu mekanisme pengecekan dan perimbangan dalam mengelola perusahaan.
Kewajiban untuk memiliki Komisaris Independen dan Komite Audit sebagaimana yang ditetapkan oleh Bursa Efek Jakarta, merupakan salah implementasi prinsip ini. Tepatnya, berupaya memberdayakan fungsi pengawasan Dewan Komisaris. Beberapa bentuk implementasi lain dari prinsip accountability antara lain:

 Praktek Audit Internal yang Efektif, serta
 Kejelasan fungsi, hak, kewajiban, wewenang dan tanggung jawab dalam anggaran dasar perusahaan dan Statement of Corporate Intent (Target Pencapaian Perusahaan di masa depan)

Bila prinsip accountability ini diterapkan secara efektif, maka ada kejelasan fungsi, hak, kewajiban, wewenang, dan tanggung jawab antara pemegang saham, dewan komisaris, serta direksi. Dengan adanya kejelasan inilah maka perusahaan akan terhindar dari kondisi agency problem (benturan kepentingan peran).

4. Responsibility (Pertanggungjawaban)
Pertanggungjawaban perusahaan adalah kesesuaian (patuh) di dalam pengelolaan perusahaan terhadap prinsip korporasi yang sehat serta peraturan perundangan yang berlaku. Peraturan yang berlaku di sini termasuk yang berkaitan dengan masalah pajak, hubungan industrial, perlindungan lingkungan hidup, kesehatan/ keselamatan kerja, standar penggajian, dan persaingan yang sehat.
Beberapa contoh mengenai hal ini dapat dijelaskan sebagai berikut :

 Kebijakan sebuah perusahaan makanan untuk mendapat sertifikat “HALAL”. Ini merupakan bentuk pertanggungjawaban kepada masyarakat. Lewat sertifikat ini, dari sisi konsumen, mereka akan merasa yakin bahwa makanan yang dikonsumsinya itu halal dan tidak merasa dibohongi perusahaan. Dari sisi Pemerintah, perusahaan telah mematuhi peraturan perundang-undangan yang berlaku (Peraturan Perlindungan Konsumen). Dari sisi perusahaan, kebijakan tersebut akan menjamin loyalitas konsumen sehingga kelangsungan usaha, pertumbuhan, dan kemampuan mencetak laba lebih terjamin, yang pada akhirnya memberi manfaat maksimal bagi pemegang saham.
 Kebijakan perusahaan mengelola limbah sebelum dibuang ke tempat umum. Ini juga merupakan pertanggungjawaban kepada publik. Dari sisi masyarakat, kebijakan ini menjamin mereka untuk hidup layak tanpa merasa terancam kesehatannya tercemar. Demikian pula dari sisi Pemerintah, perusahaan memenuhi peraturan perundang-undangan lingkungan hidup. Sebaliknya dari sisi perusahaan, kebijakan tersebut merupakan bentuk jaminan kelangsungan usaha karena akan mendapat dukungan pengamanan dari masyarakat sekitar lingkungan.

Etika auditor

November 26, 2010

• Tujuan
Tujuan perumusan kode etik auditor ini untuk memacu pencapaian (tercapainya) budaya etis di kalangan auditor mutu akademik internal. Kode etik ini diperlukan oleh auditor mutu akademik internal untuk menumbuhkan kepercayaan bagi/terhadap auditor yang akan melaksanakan tugas audit mutu akademik.
• Komponen
Kode etik auditor ini terdiri atas dua komponen, yaitu: (1) azas kode etik audit akademik dan (2) perilaku auditor akademik, yang menggambarkan norma perilaku yang perlu dimiliki oleh auditor akademik.
Kode etik ini membantu para auditor mutu akademik internal untuk menafsirkan azas-azas kode etik audit mutu akademik ke dalam penerapan praktis dan dimaksudkan untuk memandu auditor dalam berperilaku etis. Kode etik ini berlaku untuk perorangan dan atau kelompok yang melaksanakan audit mutu akademik.
• Azas Kode Etik Audit Mutu Akademik
Auditor harus menerapkan dan memegang teguh 5 azas, yaitu : (1) Azas Integritas; (2) Azas Objektivitas, (3) Azas Kerahasiaan, (4) Azas Kompetensi, dan (5) Azas Independen. Azas-azas kode etik audit mutu akademik di atas melandasi sikap dan perilaku auditor akademik dalam menjalankan tugasnya.
• Perilaku Auditor Mutu Akademik
Perilaku yang harus ditunjukkan oleh auditor akademik mencakup hal-hal sebagai berikut.
1. Menjaga Integritas
Integritas auditor mutu akademik akan menumbuhkan kepercayaan yang selanjutnya (pada gilirannya) akan menyebabkan kepatuhan pada keputusan yang dibuat, sehingga auditor harus : (1) melaksanakan audit dengan jujur dan bertanggung jawab, (2) mematuhi Piagam Audit dan membuat laporan audit sesuai aturan yang berlaku, (3) menghindari tindakan yang mendiskreditkan profesi auditor atau mendiskreditkan organisasi teraudit, dan (4) menghormati dan mendukung terlaksananya tujuan audit.
2. Menjaga Objektivitas
Auditor mempunyai objektivitas profesional yang tertinggi dalam mengumpulkan, mengevaluasi, dan menyampaikan informasi tentang aktivitas atau proses yang sedang diaudit. Auditor membuat evaluasi apa adanya dari semua keadaan yang relevan dan tidak terpengaruh oleh kepentingan perorangan atau tidak terpengaruh oleh pihak-pihak lain dalam mengambil keputusan, sehingga auditor harus : (1) menghindari aktivitas yang dapat merusak objektivitas audit mutu akademik, (2) menolak pemberian apapun yang dapat merusak kemampuannya untuk berlaku adil, dan (3) melaporkan semua fakta hasil audit (yang
seharusnya dilaporkan).
3. Menjaga Kerahasiaan
Auditor tidak akan menyampaikan informasi kepada semua pihak yang tidak berhak, sehingga auditor harus : (1) menjaga kerahasiaan informasi yang diperoleh dalam melaksanakan tugas, dan (2) menghindari penyalahgunaan informasi yang diperolehnya untuk keuntungan pribadi/kelompok atau menggunakan informasi dengan cara yang melawan hukum atau yang merugikan tujuan dan etika kelembagaan.
4. Memiliki Kompetensi
Auditor menerapkan semua pengetahuan, keterampilan, dan pengalamannya dalam melaksanakan audit mutu akademik, sehingga auditor harus : (1) menguasai (mempunyai) pengetahuan, keterampilan dan pengalaman audit untuk melaksanakan kegiatan audit, (2) melaksanakan pelayanan audit akademik sesuai dengan Standar dan Manual Prosedur Audit Mutu Akademik Internal, (3) Auditor dituntut selalu meningkatkan kemampuan, efektivitas dan mutu layanannya.
5. Memelihara Independensi
Untuk menjaga independensi, Auditor harus bebas dari campur tangan pihak-pihak lain, sehingga auditor harus : (1) bebas dari pengaruh setiap pekerjaan dalam bidang yang diaudit atau yang pernah menjadi tanggungjawabnya, (2) tidak memihak kepada siapapun, dan (3) tidak terlibat dalam pertentangan kepentingan dengan teraudit.
• Sanksi.
Auditor yang tidak mematuhi (melanggar) kode etik auditor mutu akademik akan dinilai dan ditindak sesuai prosedur penegakan disiplin yang berlaku.
• Prosedur Penegakan Disiplin
Apabila Direktur PPs-Unhas menerima laporan tertulis dan resmi mengenai adanya pelanggaran kodek etik auditor mutu akademik, Direktur PPs-Unhas akan melaksanakan penegakan disiplin sebagai berikut : (1) Direktur PPs-Unhas membentuk Komisi Etika
Auditor yang terdiri dari 5 orang, serta bertugas untuk jangka waktu 2 bulan, (2) Komisi Etika Auditor segera mempelajari isi laporan tersebut, (3) Komisi Etika Auditor mengadakan rapat untuk mendengarkan klarifikasi auditor terlapor dan juga pelapor secara terpisah (dengan mengundang auditor terlapor untuk melakukan klarifikasi, serta mengundang pelapor), (4) Setelah mendengarkan penjelasan terlapor dan pelapor, apabila tidak terbukti dan ada kesepakatan kedua belah pihak, maka prosedur pemeriksaan tidak dilanjutkan, (5) Apabila terbukti ada pelanggaran kode etik auditor akademik, maka auditor terlapor segera memperbaiki laporan yang dibuatnya, (6) Komisi Etika Auditor melaporkan hasil kerjanya kepada Direktur PPs-Unhas, dan Sanksi dari Direktur PPs-Unhas berupa : (i) peringatan lisan, (ii) peringatan tertulis pertama, kedua dan ketiga, (iii) pemberhentian sementara sebagai auditor untuk jangka waktu tertentu, dan (iv) pemberhentian sebagai auditor.

Standar Auditing

Oktober 13, 2010

PENGERTIAN DAN STANDAR AUDIT
Salah satu pengertian standar menurut Kamus Besar Bahasa Indonesia adalah ukuran tertentu yang dipakai sebagai patokan. Standar antara lain diperlukan sebagai :
1. Ukuran mutu
2. Pedoman kerja
3. Batas tanggung jawab
4. Alat pemberi perintah
5. Alat pengawasan
6. Kemudahan bagi umum
Standar yang digunakan sebagai ukuran pada umumnya diperlukan pada pekerjaan yang memiliki ciri :
1. Menyangkut kepentingan orang banyak
2. Mutu hasilnya ditentukan
3. Banyak orang (pekerja) terlibat
4. Sifat dan mutu pekerjaan sama
5. Ada organisasi yang mengatur
Standar merupakan kriteria atau ukuran mutu kinerja yang harus dicapai, berbeda dengan prosedur yang merupakan urutan tindakan yang harus dilaksanakan untuk mencapai suatu standar tertentu. Standar audit merupakan ukuran mutu pekerjaan audit yang ditetapkan oleh organisasi profesi audit, yang merupakan persyaratan minimum yang harus dicapai auditor dalam melaksanakan tugas auditnya. Standar audit diperlukan untuk menjaga mutu pekerjaan audit.mutu audit perlu dijaga supaya profesi auditor tetap mendapat kepercayaan dari masyarakat. Untk meyakinkan pembaca laporan audit, maka auditor harus mencantumkan dalam laporannya bahwa audir telah dilaksanakan sesuai dengan stndar audit yang berlaku.

Etika Profesi

Oktober 13, 2010

BEBERAPA PENGERTIAN DALAM ETIKA PROFESI

1.1 Pengertian Etika dan Etika Profesi
Kata etik (atau etika) berasal dari kata ethos (bahasa Yunani) yang berarti karakter, watak kesusilaan atau adat. Sebagai suatu subyek, etika akan berkaitan dengan konsep yang dimiliki oleh individu ataupun kelompok untuk menilai apakah tindakan-tindakan yang telah dikerjakannya itu salah atau benar, buruk atau baik. Menurut Martin [1993], etika didefinisikan sebagai “the discipline which can act as the performance index or reference for our control system”.
• Etika adalah refleksi dari apa yang disebut dengan “self control”, karena segala sesuatunya dibuat dan diterapkan dari dan untuk kepentingan kelompok sosial (profesi) itu sendiri.
• Kehadiran organisasi profesi dengan perangkat “built-in mechanism” berupa kode etik profesi dalam hal ini jelas akan diperlukan untuk menjaga martabat serta kehormatan profesi, dan di sisi lain melindungi masyarakat dari segala bentuk penyimpangan maupun penyalah-gunaan keahlian (Wignjosoebroto, 1999).
• Sebuah profesi hanya dapat memperoleh kepercayaan dari masyarakat, bilamana dalam diri para elit profesional tersebut ada kesadaran kuat untuk mengindahkan etika profesi pada saat mereka ingin memberikan jasa keahlian profesi kepada masyarakat yang memerlukannya.
1.2 Etika dan Estetika
• Etika disebut juga filsafat moral adalah cabang filsafat yang berbicara tentang praxis (tindakan) manusia. Etika tidak mempersoalkan keadaan manusia, melainkan mempersoalkan bagaimana manusia harus bertindak. Tindakan manusia ini ditentukan oleh bermacam-macam norma.
• Norma ini masih dibagi lagi menjadi norma hukum, norma moral, norma agama dan norma sopan santun. Norma hukum berasal dari hukum dan perundangundangan, norma agama berasal dari agama sedangkan norma moral berasal dari suara batin. Norma sopan santun berasal dari kehidupan sehari-hari sedangkan norma moral berasal dari etika.
1.3 Etika dan Etiket
Etika (ethics) berarti moral sedangkan etiket (etiquette) berarti sopan santun. Persamaan antara etika dengan etiket yaitu:
• Etika dan etiket menyangkut perilaku manusia. Istilahtersebut dipakai mengenai manusia tidak mengenai binatang karena binatang tidak mengenal etika maupun etiket.
• Kedua-duanya mengatur perilaku manusia secara normatif artinya memberi norma bagi perilaku manusia dan dengan demikian menyatakan apa yag harus dilakukan dan apa yang tidak boleh dilakukan. Justru karena sifatnya normatif maka kedua istilah tersebut sering dicampuradukkan.

etika profesi akuntansi

Oktober 4, 2010

Aturan Etika Profesi Akuntansi IAI

Kode Etik Ikatan Akuntan Indonesia dimaksudkan sebagai panduan dan aturan bagi seluruh anggota, baik yang berpraktik sebagai akuntan publik, bekerja di lingkungan dunia usaha, pada instansi pemerintah, maupun di lingkungan dunia pendidikan dalam pemenuhan tanggung-jawab profesionalnya. .
Tujuan profesi akuntansi adalah memenuhi tanggung-jawabnya dengan standar profesionalisme tertinggi, mencapai tingkat kinerja tertinggi, dengan orientasi kepada kepentingan publik. Untuk mencapai tujuan terse but terdapat empat kebutuhan dasar yang harus dipenuhi:
Kredibilitas. Masyarakat membutuhkan kredibilitas informasi dan sistem informasi.
• Profesionalisme. Diperlukan individu yang dengan jelas dapat diidentifikasikan oleh pemakai jasa Akuntan sebagai profesional di bidang akuntansi.
• Kualitas Jasa. Terdapatnya keyakinan bahwa semua jasa yang diperoleh dari akuntan diberikan dengan standar kinerja tertinggi.
• Kepercayaan. Pemakai jasa akuntan harus dapat merasa yakin bahwa terdapat kerangka etika profesional yang melandasi pemberian jasa oleh akuntan.
Kode Etik Ikatan Akuntan Indonesia terdiri dari tiga bagian: (1) Prinsip Etika, (2) Aturan Etika, dan (3) Interpretasi Aturan Etika. Prinsip Etika memberikan kerangka dasar bagi Aturan Etika, yang mengatur pelaksanaan pemberian jasa profesional oleh anggota. Prinsip Etika disahkan oleh Kongres dan berlaku bagi seluruh anggota, sedangkan Aturan Etika disahkan oleh Rapat Anggota Himpunan dan hanya mengikat anggota Himpunan yang bersangkutan. Interpretasi Aturan Etika merupakan interpretasi yang dikeluarkan oleh Badan yang dibentuk oleh Himpunan setelah memperhatikan tanggapan dari anggota, dan pihak-pihak berkepentingan lainnya, sebagai panduan dalam penerapan Aturan Etika, tanpa dimaksudkan untuk membatasi lingkup dan penerapannya.
Kesimpulan
Setiap profesi yang menyediakan jasanya kepada masyarakat memerlukan kepercayaan dari masyarakat yang dilayaninya. Kepercayaan masyarakat terhadap mutu jasa akuntan publik akan menjadi lebih tinggi, jika profesi tersebut menerapkan standar mutu tinggi terhadap pelaksanaan pekerjaan profesional yang dilakukan oleh anggota profesinya. Aturan Etika Kompartemen Akuntan Publik merupakan etika profesional bagi akuntan yang berpraktik sebagai akuntan publik Indonesia. Aturan Etika Kompartemen Akuntan Publik bersumber dari Prinsip Etika yang ditetapkan oleh Ikatan Akuntan Indonesia. Dalam konggresnya tahun 1973, Ikatan Akuntan Indonesia (IAI) untuk pertama kalinya menetapkan kode etik bagi profesi akuntan Indonesia, kemudian disempurnakan dalam konggres IAI tahun 1981, 1986,1994, dan terakhir tahun 1998. Etika profesional yang dikeluarkan oleh Ikatan Akuntan Indonesia dalam kongresnya tahun 1998 diberi nama Kode Etik Ikatan Akuntan Indonesia.
Akuntan publik adalah akuntan yang berpraktik dalam kantor akuntan publik, yang menyediakan berbagai jenis jasa yang diatur dalam Standar Profesional Akuntan Publik, yaitu auditing, atestasi, akuntansi dan review, dan jasa konsultansi. Auditor independen adalah akuntan publik yang melaksanakan penugasan audit atas laporan keuangan historis yang menyediakan jasa audit atas dasar standar auditing yang tercantum dalam Standar Profesional Akuntan Publik. Kode Etik Ikatan Akuntan Indonesia dijabarkan ke dalam Etika Kompartemen Akuntan Publik untuk mengatur perilaku akuntan yang menjadi anggota IAI yang berpraktik dalam profesi akuntan publik.
Etika Profesi Akuntansi
Pendahuluan
Latar Belakang
Timbul dan berkembangnya profesi akuntan publik di suatu negara adalah sejalan dengan berkembangnya perusahaan dan berbagai bentuk badan hukum perusahaan di negara tersebut. Jika perusahaan-perusahaan di suatu negara berkembang sedemikian rupa sehingga tidak hanya memerlukan modal dari pemiliknya, namun mulai memerlukan modal dari kreditur, dan jika timbul berbagai perusahaan berbentuk badan hukum perseroan terbatas yang modalnya berasal dari masyarakat, jasa akuntan publik mulai diperlukan dan berkembang. Dari profesi akuntan publik inilah masyarakat kreditur dan investor mengharapkan penilaian yang bebas tidak memihak terhadap informasi yang disajikan dalam laporan keuangan oleh manajemen perusahaan.
Profesi akuntan publik menghasilkan berbagai jasa bagi masyarakat, yaitu jasa assurance, jasa atestasi, dan jasa nonassurance. Jasa assurance adalah jasa profesional independen yang meningkatkan mutu informasi bagi pengambil keputusan. Jasa atestasi terdiri dari audit, pemeriksaan (examination), review, dan prosedur yang disepakati (agreed upon procedure). Jasa atestasi adalah suatu pernyataan pendapat, pertimbangan orang yang independen dan kompeten tentang apakah asersi suatu entitas sesuai dalam semua hal yang material, dengan kriteria yang telah ditetapkan. Jasa nonassurance adalah jasa yang dihasilkan oleh akuntan publik yang di dalamnya ia tidak memberikan suatu pendapat, keyakinan negatif, ringkasan temuan, atau bentuk lain keyakinan. Contoh jasa nonassurance yang dihasilkan oleh profesi akuntan publik adalah jasa kompilasi, jasa perpajakan, jasa konsultasi.
Secara umum auditing adalah suatu proses sistematik untuk memperoleh dan mengevaluasi bukti secara objektif mengenai pernyataan tentang kejadian ekonomi, dengan tujuan untuk menetapkan tingkat kesesuaian antara pernyataan tersebut dengan kriteria yang telah ditetapkan, serta penyampaian hasil-hasilnya kepada pemakai yang berkepentingan. Ditinjau dari sudut auditor independen, auditing adalah pemeriksaan secara objektif atas laporan keuangan suatu perusahaan atau organisasi yang lain dengan, tujuan untuk menentukan apakah laporan keuangan tersebut menyajikan secara wajar keadaan keuangan dan hasil usaha perusahaan atau organisasi tersebut.
Profesi akuntan publik bertanggung jawab untuk menaikkan tingkat keandalan laporan keuangan perusahaan-perusahaan, sehingga masyarakat keuangan memperoleh informasi keuangan yang andal sebagai dasar untuk memutuskan alokasi sumber-sumber ekonomi.
Pembahasan
Pengertian etika
• Menurut Kamus Besar Bhs. Indonesia (1995) Etika adalah Nilai mengenai benar dan salah yang dianut suatu golongan atau masyarakat
• Etika adalah Ilmu tentang apa yang baik dan yang buruk, tentang hak dan kewajiban moral
• Menurut Maryani & Ludigdo (2001) “Etika adalah Seperangkat aturan atau norma atau pedoman yang mengatur perilaku manusia, baik yang harus dilakukan maupun yang harus ditinggalkan yang di anut oleh sekelompok atau segolongan masyarakat atau profesi”
Dari asal usul kata, Etika berasal dari bahasa Yunani ‘ethos’ yang berarti adat istiadat/ kebiasaan yang baik Perkembangan etika yaitu Studi tentang kebiasaan manusia berdasarkan kesepakatan, menurut ruang dan waktu yang berbeda, yang menggambarkan perangai manusia dalam kehidupan pada umumnya
2.1.1 Fungsi Etika
1. Sarana untuk memperoleh orientasi kritis berhadapan dengan pelbagai moralitas yang membingungkan.
2. Etika ingin menampilkanketrampilan intelektual yaitu ketrampilan untuk berargumentasi secara rasional dan kritis.
3. Orientasi etis ini diperlukan dalam mengabil sikap yang wajar dalam suasana pluralisme
2.1.2 Faktor-faktor Yang Mempengaruhi Pelanggaran Etika :
1. Kebutuhan Individu
2. Tidak Ada Pedoman
3. Perilaku dan Kebiasaan Individu Yang Terakumulasi dan Tak Dikoreksi
4. Lingkungan Yang Tidak Etis
5. Perilaku Dari Komunitas
2.1.3 Sanksi Pelanggaran Etika :
1. Sanksi Sosial
Skala relatif kecil, dipahami sebagai kesalahan yangdapat ‘dimaafkan’
2. Sanksi Hukum
Skala besar, merugikan hak pihak lain.
2.1.4 Jenis-jenis Etika
1. Etika umum yang berisi prinsip serta moral dasar
2. Etika khusus atau etika terapan yang berlaku khusus.
Etika khusus ini masih dibagi lagi menjadi etika individual dan etika sosial.•
Etika sosial dibagi menjadi:•
o Sikap terhadap sesama;
o Etika keluarga
o Etika profesi misalnya etika untuk pustakawan, arsiparis, dokumentalis, pialang informasi
o Etika politik
o Etika lingkungan hidupserta
o Kritik ideologi Etika adalah filsafat atau pemikiran kritis rasional tentang ajaran moral sedangka moral adalah ajaran baik buruk yang diterima umum mengenai perbuatan, sikap, kewajiban dsb. Etika selalu dikaitkan dengan moral serta harus dipahami perbedaan antara etika dengan moralitas.

jurnal Akuntansi-B.Inggris

Mei 12, 2010

The Asean Stock Market Integration:
The Effect of the 2007 Financial Crisis on the Asean Stock Indices’
Movements
Adwin Surja Atmadja
Faculty of Economics, Petra Christian University
E-mail: aplin@peter.petra.ac.id
ABSTRACT
This study attempts to examine the existence of cointegration relationship and the short
run dynamic interaction among the five ASEAN stock market indices in the period of before
and during the 2007 financial crisis. The multivariate time series analysis frameworks are
employed to the series in both sub-sample periods in order to answer the hypotheses.The
study finds two cointegrating vectors in the series before the financial crisis period, however
it fails to detect any cointegrating vector in the period of financial crisis. Granger causality
tests applied to the series reveal that number of significant causal linkages between two
variables increase during the crisis period. Moreover, the accounting innovation analysis
shows an increase in the explanatory power of an endogenous variable to another within the
system during the crisis period, indicating that the contagious effect of the 2007-US financial
crisis has entered into the ASEAN capital market, and significantly influenced the regional
indices’ movements.
Keywords: ASEAN, stock market integration, the 2007 financial crisis, regional indices’
movements.
INTRODUCTION
Liberalization of the five ASEAN (Indonesia,
Malaysia, the Philippines, Singapore, and
Thailand) financial markets in 1980s resulted in
enormous capital inflows to this region. By opening
their national borders for foreign investors, the
countries’ financial markets were overwhelmed by
foreign capital in both foreign direct and portfolio
investments giving significant support to their
rapid domestic economic development, as well as
enjoyed rapid financial markets expansion in the
beginning of 1990s. Capital inflows have been
crucial to the rapid – sustained growth in ASEAN
countries (Sachs and Larrain, 1993:577) at that
time, since domestic saving, as commonly in
developing countries, had little role as development
funding.
Triggered by the sharp depreciation of the
Thai baht in the midst of 1997, the disastrous
effects of the 1997 financial crisis were broadly
spread out to the countries’ financial markets
which were dominated by bank loan and portfolio
investment, not by foreign direct investment
(DFAT, 1999:29). The crisis then extensively
affected the world financial markets through its
contagion effects. Market capitalization of the
countries’ stock market was largely contracted due
to a deep depreciation in their stock prices causing
their stock indices then sharply plunged.
However, the downturn in the five ASEAN
rebounded in 1999. After the sharp output
contraction in 1998, growth returned in that year
as depreciated currencies spurred higher exports
(Krugman and Obstfeld, 2003:693). Following the
appreciation of regional currencies in the second
semester of the year, the regional capital and
financial markets started to recover. The regional
stock market indices increased around 42.46% on
average compared to those from two years before
(calculated from IFS 2004). This might indicate
that investors’ confidence started to recover and
they began to invest in the five ASEAN.
During ten years after, the ASEAN’s
economies steadily grew to their new equilibrium.
As a market indicator, the ASEAN capital market
indices apparently fluctuated in a relatively narrow
range dominantly due to small internal shocks in
the short run, but stably moved with positive
trends in the long run. These all mirror that the
ASEAN markets were relatively stable during the
time periods, and their economies were just on the
right tracks.
However, in the second semester of 2007 the
countries experienced significant shocks in their
capital markets due to a contagious effect of the US
financial market turmoil. At the time, the US
financial market deeply suffered from the most
1
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2
significant economic shocks initiated by the subprime
mortgage crisis leading to the downturn in
housing market, and then worsened by the spike in
commodity prices (Yellen 2008:1). The devastating
effects of the 2007 financial crisis in the US then
widely spread throughout the world.
From the facts above, the 2007 financial crisis
may have significant consequences on the variation
of the countries’ stock indices that probably
different with those in non crisis era. The financial
crisis could possibly cause the regional indices
deviate from their long run equilibrium, and the
behaviour of the indices’ movements may be
different with those before. All possibilities may
happen in the regional market depended on how
significant the impact of the financial crisis hit the
market. Therefore, this study will empirically
examine how the 2007 financial crisis has taken
into effect on the five ASEAN stock indices’
movements. To be more specific, this study
attempts to observe the existing of cointegrating
relationships among the five ASEAN stock indices
in the periods of before (pre) and during the 2007
financial crisis in order to portray the long run
interrelations among the indices in the both
periods. The aim is also to answer how and to what
extent the stock indices dynamically interact with
each other in the short run during the given
periods.
CONCEPT OF FINANCIAL MARKET OR
STOCK MARKET INTEGRATION
The basic theoretical concept of financial
market or stock market integration is adopted from
the law of one price. In integrated financial
markets, the assets with the same risk in different
markets will result in the same yield when
measured in a common currency (Stulz 1981:924-
5). However, if the yields are different across the
markets, the arbitrage process will play an
important role in eliminating the differences.
Operationally capital markets integration refers to
the extent that markets’ participants are enabled
and obligated to take notice of events occurring in
other markets by using all available information
and opportunities, while financial market
integration is defined in terms of price
interdependence between markets (Kenen 1976:9).
Moreover, stock market integration is affected by
some factors (Roca 2000:14), such as:
1. Economic integration, which means that the
more integrated the economies of countries, the
more integrated their equity markets (Eun and
Shim 1989: 256).
2. Multiple listing of stocks. This implies that a
shock in a particular stock market can be
transmitted to other stock market through
shares listed in both markets.
3. Regulatory and information barriers. The
higher the barriers, the lower the degree of
stock market integration.
4. Institutionalisation and securitisation. As
institutions are more willing to transfer funds
overseas to increase their diversification
opportunities, the integration will be promoted.
5. Market contagion. The prices between stock
markets can move together due to a contagion
effect (King and Wadwhani 1990:5), and this
contagion effect determines significantly the
dynamic relationships between international
stock markets (Climent and Meneu, 2003:111).
However, in emerging stock markets, this effect
might be smaller than what is widely perceived
(Pretorius 2002:103).
Much research has been done, mainly by using
a cointegration analytical framework, to find and
analyse the existence of integration in stock
market across countries. The results are different
depending on where, when, and how the research
has being conducted. The cointegration analytical
framework has been widely applied to examine the
integration of stock markets across countries. Once
a cointegration vector is found among two or more
stock markets, it indicates the existence of a long
run relationship among them. Thus, stock price
movements in one equity market will affect
another in other markets.
A research conducted by Chung and Liu
(1994:55) found two cointegration vectors between
the U.S and larger Asia Pacific stock markets.
Palac-McMiken (1997:299) also reveals the
existence of cointegration in ASEAN markets
(Malaysia, Singapore, Thailand, and the
Philippines), except Indonesia, during 1987 to
1995. Both results were confirmed by Masih and
Masih (1999:275) who report that some of ASEAN
countries (Thailand, Malaysia, and Singapore)
have a high degree of interdependence with other
Asian (Hong Kong and Japan) and developed (the
U.S. and the U.K.) stock markets. Furthermore,
they also find one cointegration vector among
several major Asian stock markets (Hong Kong,
Korea, Singapore, and Taiwan) and major
developed markets (Masih and Masih 2001: 580-1).
Interestingly, Pretorius (2002:103) reports that
the degree of bilateral trade and the industrial
production growth differential significantly
explained the correlation between two equity
markets, and that the stock markets of countries in
the same region are more interdependent than
those in different regions. Consistent with this
finding, Roca (2000:145) finds the existence of
Atmadja: The Asean Stock Market Integration
3
interdependency among all the ASEAN stock
markets in the short run. However, in contrast to
short run interdependency, he indicates that there
was no cointegration among ASEAN countries as a
group during 1988-1995 and that those stock
markets were not significantly related to each
other in the long run.
Chan, Gup and Pan (1992:289) and DeFusco,
Geppert and Tsetsekos (1996:343) also mention
that there is no cointegration between the U.S and
several Asian emerging stock markets (Hong Kong,
Taiwan, Singapore, Korea, Malaysia, Thailand,
and the Philippines) in the 1980s and early 1990s.
However, these findings somewhat contradicts
with those of Chung et al. (1994) and Masih et al.
(1999). This then implies that the interdependence
among stock markets is not stable over time. For
example, Hung and Cheung (1995:286) assert that
there is no cointegration among stock markets in
some Asia-Pacific countries (Malaysia, Hong Kong,
Korea, Singapore, and Taiwan). However, when
they used US dollar denominated stock prices, it
was reported that those stock markets were
cointegrated after, but not before, the 1987 stock
crash.
Arshanapalli and Doukas (1993:206) also
mention the instability of stock market
interdependence when they tested the effect of
inclusion or omission of the data for the 1987 crisis
and revealed that that it affects the results. They
conclude that the stock markets were highly
integrated during the crisis. Furthermore,
Arshanapalli, Doukas and Lang (1995:72) show
that after the 1987 crisis the stock markets in
emerging markets (Malaysia, the Philippines, and
Thailand) and developed markets (Hong Kong,
Singapore, the U.S., and Japan) are more
interdependent as they found cointegration in the
post-crisis period, but not in the pre-crisis period.
Other researchers, Liu, Pan and Shieh (1998: 59)
also confirm that there is an increase in the
interdependence within Asian-Pacific regional
markets and the stock markets in general post-the
1987 crisis. Similarly, Sheng and Tu (2000:245)
document one cointegration vector between the
U.S. and several Asian stock markets (Taiwan,
Malaysia, China, Thailand, Indonesia, South
Korea, the Philippines, Australia, Japan, Hong
Kong, and Singapore) during the crisis, but none in
the year before the crisis, when they observed the
stock markets using daily data.
Finally, a research recently conducted by
Yang, Kolari and Min (2003:478) examined the
long-run relationship and short-run dynamic
causal linkages among the U.S, Japanese, and ten
Asian emerging markets using daily data of 1997-
1998 periods. They confirm that the stock markets
of those countries have been more integrated after
the 1997 Asian financial crisis than before the
crisis. Both long-run cointegration relationship and
short-run causal linkages among those markets
become more significant during the crisis. These
findings also confirm that the degree of integration
among those countries tends to change over time.
Several points that may be drawn form the
literature review. The implication is that
liberalization of the financial sector in many
countries has caused world or regional stock
markets to be more integrated. Empirical evidence
is given by the presence of cointegration vectors
and significant short-run causal linkages. It is
worth noting that the stock markets of countries in
the same region may be more interdependent than
those in different regions.
RESEARCH METHODOLOGY
Basically, a stock market price index or stock
market index is a portfolio of individual stocks. The
index level corresponds to some average of the
price levels of individual shares. Changes in the
index level give rise to market returns. Thus, the
stock market index, which can be viewed simply as
a portfolio of shares, can commonly be use as an
indicator of the market performance. There are
several factors that determine the level of the
index, such as breadth of index, weighting system,
capitalization adjustment, and dividend effect
(Brailsford Heaney and Bilson 2004:68).
The stock market index of a country may also
be an indicator of short-term portfolio investment
movement in the country. An upward trend of a
stock market index means that there is an increase
in demand of the listed shares in the market. This
indicated that investors are attracted to buy shares
and invest their fund in the country. On the other
hand, a downward trend movement of a stock
market index indicates that the investors are
unlikely to continuously hold the listed shares.
Hence, stock market movements may reflect the
attractiveness of a country for investments,
especially for portfolio investments.
In this study, the daily closing stock price
indices of the five ASEAN countries, which are
Jakcomp of Indonesia; KLSE of Malaysia; PSEi of
the Philippines; STI of Singapore; and SET
Composite of Thailand, are employed as
measurement of the countries’ daily stock index
movements in the periods of before and during the
2007 financial crisis.
Some previous research (Arshanapalli et al,
1993, Chung et al, 1994, Arshanapalli et al, 1995,
Liu et al,1998, Masih et al., 1999, Masih et al,
2001) document that stock markets in the Asian
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4
region are interdependent not only among
themselves, but also with some of the developed
market. Furthermore, those stock markets are
even more interdependent during and after the
financial crisis (Sheng et al 2000; Yang et al 2003)
In the case of the ASEAN, Palac-McMiken
(1997:299) reports the existence of cointegration in
the countries’ stock markets, except Indonesia,
before the 1997 crisis. Yang et al (2003:478)
confirm that both long-run cointegration
relationship and short-run causal linkages among
those markets become more significant during the
crisis period. In contrast, Roca (2000:145) finds the
existence of interdependency among the five
ASEAN’s stock markets in the short run, but not
significantly related in the long run before the 1997
crisis.
Based on these findings, it is hypothesized that
the ASEAN stock indices would have long run
cointegration relationship and short run dynamic
interaction, and that the relationship and the
interaction would be more significant during the
2007 financial crisis.
All daily price index data of the five ASEAN
during the observation periods are obtained from
the Thomson Financial. The index data of all
variables then will be transformed into natural
logarithm forms before conducting the analyses.
In order to examine the movements of the
indices in both periods, the data are then separated
into two sub-sample periods, which are the periods
of: 1) Before the 2007 financial crisis (pre crisis),
which cover the period of Jan 2000 – June 2007, 2)
During the 2007 financial crisis, which cover the
period of July 2007 – May 2009, as it is stated in
several publications (http://en.wikipedia.org,www.
globalissues.org,www.atypon-link.com)
The two most appropriate models that one of
which may suitable for this study are VAR and
VECM. In the Vector autoregressive model (VAR)
all of the variables are endogenous, and
symmetrically treated. A VAR could be very large,
however the simplest VAR model, in standard
form, could be written as (Enders, 2004:265):
Yt = a10 + a11Yt-1 + a12 Zt-1 + eYt.
Zt = a20 + a21Yt-1 + a22 Zt-1 + εZt.
The VAR requires that all variables be
stationary and the appropriate lag length is data
driven (Brooks 2002:333). There are several
available tests for testing for a unit root, the most
common is the Augmented Dicky-Fuller (ADF)
test. Non-stationary variables may be made
stationary by differencing or detrending process.
To define the appropriate lag length, some
tests of information criteria that will be applied in
this study include the likelihood ratio test; Akaike
Information Criterion (AIC); and Schwarz
Bayesian Criterion (SC).
The likelihood ratio test is based on asymptotic
theory and is an F-type approximation. This test
actually compares a restricted VAR (less lags) to an
unrestricted VAR (more lags). Thus, the null
hypothesis of this test is that the restricted model
is correct. However, the shortcoming of this test is
that it may not be useful in small samples. In
addition, the likelihood ratio test is only valid when
the restricted model is tested (Enders 2004:283).
Because of the limitations of the likelihood
ratio test, multivariate generalization of AIC and
SC may be the most suitable alternatives. The
minimum values of AIC and/or SC may validly
indicate the appropriate lags length, as long as the
model’s residual has no serial correlation problem.
Otherwise, the lag length may be too short. Thus, it
is necessary to re-estimate the model using lag
length that yield serially uncorrelated (Enders
2004:338).
In VAR, a block causality test will be used to
examine whether the lags of one variable enter into
the equation for another variable (Enders
2004:283). A variable (y1) is said to be a grangercause
of another (y2) if the present value of y2 can
be predicted with greater accuracy by using past
values of y1, all other information being identical
(Thomas 1997:461). If y1 granger-causes y2, then
the parameters of lags of y1, βi’s, should not equal
zero in the equation of y2. However, it is worth
noting that granger-causality basically means a
correlation between the current value of one
variable and the past (lags) value of others. It does
not mean that movements of one variable
physically cause movements of another (Brooks,
2002:240). Granger causality simply implies a
chronological ordering of movements of the series.
Therefore, it could validly be stated that changes or
movements in one variable (y2) appear to lag those
of another (y1).
The alternative model that probably suitable
to be used is the vector error correction model
(VECM) or cointegration framework analysis,
which is basically is a VAR augmented by the error
correction term (êt-1). The simplest VECM, in
general, takes the form as (Enders 2004:329):
ΔYt = α10 + αY êt-1+ Σ α11(i) ΔYt-i + Σ α12(i) ΔZt-i + εYt.
ΔZt = α20 + αZ êt-1+ Σ α21(i) ΔYt-i + Σ α22(i) ΔZt-i + εYt.
where
êt-1 = (Yt-1 – β1Z1t-1)
Thus, if the parameters of error correction
term (ECT), called speed of adjustments (αY and αZ)
in VECM, are zero, then VECM reverts to a VAR
in first differences (Enders 2004:329).
ΔYt = α10 + Σ α11(i) ΔYt-i + Σ α12(i) ΔZt-i + εYt.
ΔZt = α20 + Σ α21(i) ΔYt-i + Σ α22(i) ΔZt-i + εYt.
However, if the speed of adjustments are not
zero, the larger the speed of adjustments, the
Atmadja: The Asean Stock Market Integration
5
greater the response to previous periods’ deviation
from the long run equilibrium. Thus, a
cointegration relationship is a long term or
equilibrium phenomenon, since it is possible that
cointegrating variables may deviate from their
relationship in the short run, but their association
would return in the long run. A principal feature of
cointegrated variable is that their time paths are
influenced by the extent of any deviation from long run
equilibrium. After all, if the system is to return to long
run equilibrium, the movements of at least some of the
variables must respond to the magnitude of the
disequilibrium. (Enders 2004:328). The VECM result is
also sensitive to its lags length. Thus, it is essential
to use appropriate lag length to get the appropriate
outcomes by conducting the lag order selection
criteria (LR, AIC, or SC) tests.
Unlike VAR, cointegration refers to a linear
combination of non-stationary variables. Thus, it is
necessary to test the existence of unit roots in
observed variables using the ADF test as it is used
in VAR. Cointegration also requires that all
variables in a model be integrated of the same
order. Thus, in order to test the existence of
cointegrated variable, one may use the Engle-
Granger (EG) test, which is a residuals-based
approach, or the Johansen Cointegration test. In
the case of a cointegration relationship does not
exist, a VAR analysis in first difference will then be
the correct specification to conduct the estimation
(Enders, 2004:287).
After estimating the VECM equations, the
VEC Pairwise Granger Causality / Block Exogenity
Wald Tests will be applied to reveal whether
changes in one variable cause changes in another.
If so, then lags of variable should be significant in
the equation for the other variable. If this is the
case, it can be said that the variable grangercauses
another.
A direct interpretation of the cointegration
relations may be difficult or misleading (Lutkepohl
and Reimers 1992:53, Runkle 1987:442). As in a
traditional VAR analysis, innovation accounting,
consist of Impulse Response and Variance
Decomposition Analyses, can provide a solution to
the interpretation problem, and might be the most
appropriate method to explain the short run
dynamic structure of market linkages (Yang et al
2003:479). The analysis would give to answers
whether changes in the value of a given variable
have positive or negative effect on other variables
in the system, or how long it would take for the
effect of that variable to work through the system
(Brooks 2002:341).
A shock to the i-th variable not only directly
affects the i-th variable but is also transmitted to
all of the other endogenous variables through the
dynamic (lag) structure of the VAR. An impulse
response function traces the effect of a one-time
shock to one of the innovations on current and
future values of the endogenous variables. In other
words, impulse response analysis will trace out the
responsiveness of the dependent variables in VAR
to shocks on individual error terms. In this paper,
the generalized type of impulse responses analysis
is employed as orthogonalized impulse responses is
sensitive to the ordering of the variable in the
system. The Generalized Impulses as described by
Pesaran and Shin (1998) constructs an orthogonal
set of innovations that does not depend on the VAR
ordering. The generalized impulse responses from
an innovation to the j-th variable are derived by
applying a variable specific Cholesky factor
computed with the j-th variable at the top of the
Cholesky ordering. Dekker, Sen and Young
(2001:31) found that the generalized approach
provided more accurate results than the traditional
orthogonalized approach for both impulse response
and forecast error variance decomposition analysis
Forecast error variance decomposition,
meanwhile, refers to the proportion of the
movements in a sequence due to its own shock
versus shocks to the other variables (Enders
2004:280). This analysis separates the variation in
an endogenous variable into the component shocks
to the system. Thus, the variance decomposition
provides information about the relative importance
of each random innovation in affecting the
variables in the system. It determines how much of
the s-step ahead forecast error variance of a given
variable is explained by innovations to each
explanatory variable. A shock to the i-th variable
will not only affect that variable, but also can be
transmitted to all of the other variables in the
system. To some extent, impulse responses and
variance decompositions offer very similar
information.
EMPIRICAL RESULTS
The Period of before the 2007 Financial
Crisis
The ADF test applied to all variables at level
within the sub-sample period results in acceptence
(fail to reject) of the null hypothesis that the serries
contain unit root. The existence of a unit root in
Asian stock markets, including the ASEAN is well
established in the literature (Masih et al 1999,
2001). The examination then continues to select
the appropriate lag order. The lag orders suggested
by the three lag order selection criteria result in
serially correlated residual. Therefore, as
mentioned by Enders (2004:338), it is necessary to
re-estimate the model using all possible lag length
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until the residual is found serially uncorrelated.
After examination of all possible lag length, the
appropriate lag length is found to be six.
The Johansen Cointegration test then reveals
that there are conflicting results between max
and trace statistic as it is stated in Table 1.
However, as it is suggested by some
econometricians (Johansen and Juselius 1990;
Kasa, 1992; and Serletis and King 1997) that the
trace tends to have more power than the max
because trace takes into account all degrees of
freedom (n-r) of the smallest eigenvalues, then the
number of cointegration vectors suggested by the
trace statistic would be employed. Thus, it may
be concluded that there are two cointegrating
vectors found in the series of the sub-sample period
at 5% level of significance, meaning that the
ASEAN indices are highly interdependent and
significantly related to each other in the long run
during the pre crisis period.
Table 1. The Johansen Cointegration Test For the
sub-sample period of before the 2007
financial Crisis
Trend assumption: Linear deterministic trend
Unrestricted Cointegration Rank Test
Hypothesized
No. of CE(s)
Eigenvalue Trace
Statistic
5 Percent
Critical
Value
1 Percent
Critical
Value
None ** 0.017748 83.02299 68.52 76.07
At most 1 * 0.010864 48.13862 47.21 54.46
At most 2 0.008270 26.85945 29.68 35.65
At most 3 0.004107 10.68322 15.41 20.04
At most 4 0.001368 2.667069 3.76 6.65
Trace test indicates 2 cointegrating equation(s) at the 5% level
Trace test indicates 1 cointegrating equation(s) at the 1% level
Hypothesized
No. of CE(s)
Eigenvalue Max-
Eigen
Statistic
5 Percent
Critical
Value
1 Percent
Critical
Value
None * 0.017748 34.88437 33.46 38.77
At most 1 0.010864 21.27917 27.07 32.24
At most 2 0.008270 16.17623 20.97 25.52
At most 3 0.004107 8.016154 14.07 18.63
At most 4 0.001368 2.667069 3.76 6.65
Max-eigenvalue test indicates 1 cointegrating equation(s) at the
5% level
Max-eigenvalue test indicates no cointegration at the 1% level
The existence of cointegrating vectors resulted
from this study is somewhat consistent with
previous research conducted by Palac-McMiken
(1997:299) and Liu et al (1998:59), but contradicts
with that of Sheng et al (2000:245), in different
period of time. Thus, it can be argued that VECM
is possible to be carried out to estimate the stock
indices of the five ASEAN.
The results of the VECM estimation can be
shown in the two consecutive tables. Table 2
(APPENDIX) shows the estimated cointegrating
vectors, whereas Table 3 report the coefficient of
speed of adjustment.
Table 2. Estimated Cointegrating Vectors
Cointegrating Eq: CointEq1 CointEq2
JAKCOMP 1.000000 0.000000
KLSE 0.000000 1.000000
PSE -2.101383 1.203789
(0.32567) (0.31491)
[-6.45239] [ 3.82264]
SET -0.420384 -0.438546
(0.09796) (0.09472)
[-4.29149] [-4.62993]
STI 1.353018 -2.018991
(0.37949) (0.36695)
[ 3.56532] [-5.50208]
C 1.355101 2.913097
Note: cointegration with unrestricted intercepts and no trends.
Included observations: 1948 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
Table 3. Speed of Adjustment Parameter of the
Error Correction Term (ECT)
Error
Correction:
JAKCOMP
KLSE PSE SET STI
ect1 (α1) -0.004776 5.52E-05 0.009661 0.001528 0.004420
(0.00244) (0.00164) (0.00238) (0.00263) (0.00211)
[-1.95436] [ 0.03365] [ 4.05722] [ 0.58073] [ 2.09935]
ect2 (α2) -0.005994 -0.003282 -0.004991 0.001391 0.005767
(0.00303) (0.00203) (0.00295) (0.00326) (0.00261)
[-1.97914] [-1.61487] [-1.69136] [ 0.42634] [ 2.20980]
Note : cointegration with unrestricted intercepts and no trends
Included observations: 1948 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
As a common practice, Table 2 (APPENDIX)
shows that the first cointegrating vector is
normalized by JAKCOMP, while KLSE is
restricted to zero. Meanwhile, in the second one,
KLSE is used to normalize, while JAKCOMP is
restricted to zero. Based on t-statistic at the 5%
level of significance, JAKCOMP, PSE, SET, and
STI are found significant in the first cointegration
vector, while KLSE, PSE, SET, and STI are
significant in the second one. This means that all of
the significant indices (variables) significantly
contribute to the ASEAN indices’ long run
equilibrium.
With the same critical value of 5%, the speed of
adjustment coefficient for the first and second
cointegrating vector, for KLSE and SET are
statistically zero. This implies that both vectors
have no contribution to the convergence of these
indices to their long run paths, although SET does
have significant influence on any of the
cointegrating relationship, and KLSE affects only
the second one.
Atmadja: The Asean Stock Market Integration
7
In contrast, the speed of adjustment of
JAKCOMP, PSE, and STI are statistically
significant in both vectors. JAKCOMP has
negative influences in both cointegrating
relationship indicating a downward long run
adjustment. Conversely, STI affects the vectors
positively implying an upward long run
adjustment. In the second cointegrating vector,
JAKCOMP will react to a disequilibrium among
KLSE, PSE, SET, and STI. Thus, the vector would
contribute to the convergence of JAKCOMP to its
long run path, even though the index does not have
any significant contribution to the others return to
the long run equilibrium. PSE interestingly has
positive and negative significant impact on the first
and the second cointegration vectors, respectively.
The implication is that PSE would react positively
in the first vector, and negatively in the second one.
The existence of the cointegrating relationship
in the region during the time period could be
caused by some reasons. First, the degree of
economic integration in the ASEAN countries has
risen after the 1997 financial crisis. The
information barriers have also significantly decline
as a result of technological advance in IT
(information technology) and in the markets’
trading operating systems. The other reason is that
the degree of institutionalization and securitization
have increased in the regional market promoting
intra-regional fund transfers to increase
diversification opportunities.
After the VECM estimation is determined, the
next step is to search the existence of granger
causality among variables of each model. The
results of VEC Pairwise Granger Causality Tests
for each country are presented Table 4. Using a 5%
level of significance, the table shows only four
significant causality linkages found among the
variables in the pre crisis period. It also reveals
that none of the other ASEAN indices is
significantly granger caused JAKCOMP during the
period, vice versa. Thus, it may be concluded that
movements of the index during the period
apparently become isolated from the influence of
the others. STI experienced almost the same
condition as JAKCOMP when all other ASEAN
indices do not granger cause the index. However,
somewhat different with JAKCOMP, STI, as well
as SET, granger cause (in uni-directional form)
KLSE meaning that movements in KLSE
appeared to lag those of STI and SET. Moreover,
SET also appears to have bi-directional causality
with PSE.
As a part of the Accounting Innovation
Analysis, the impulse response analysis traces out
the responsiveness of the dependent variable in the
system to shocks to each of the variables (Brooks,
2002:341). The generalized type of the impulse
response analysis will be applied in this study to
observe short run dynamic interactions among the
variables, since orthogonalized impulse responses
is sensitive to the ordering of the variable in the
system. The complete result of the analysis is
presented in Table 5.
Table 4. VEC Pairwise Granger Causality/Block
Exogeneity Wald Tests
Dependent
variable Exclude Chi-sq Prob.
JAKCOMP KLSE 6.158533 0.4057
PSE 10.79299 0.0950
SET 9.533360 0.1457
STI 7.470766 0.2795
KLSE JAKCOMP 4.013962 0.6748
PSE 11.10882 0.0851
SET 12.83167 0.0458
STI 18.49538 0.0051
PSE JAKCOMP 12.17061 0.0583
KLSE 4.755074 0.5756
SET 40.46320 0.0000
STI 10.91111 0.0912
SET JAKCOMP 4.591306 0.5972
KLSE 10.01266 0.1241
PSE 13.22751 0.0396
STI 4.343344 0.6303
STI JAKCOMP 12.14867 0.0587
KLSE 9.910047 0.1285
PSE 8.841288 0.1827
SET 9.852119 0.1310
Table 5. The Impulse Response to Generalized One
S.D. Innovations
Response
of
Period JAKCOMP
KLSE PSE SET STI
JAKCOMP 1 0.012863 0.003140 0.002791 0.003167 0.004256
2 0.013992 0.003609 0.003377 0.003920 0.005196
3 0.013608 0.004138 0.003762 0.004475 0.005381
4 0.013862 0.004212 0.004669 0.005340 0.006202
5 0.014158 0.004193 0.005338 0.005551 0.006843
6 0.014500 0.004412 0.005403 0.006451 0.007608
7 0.014227 0.004330 0.005254 0.006773 0.007709
KLSE 1 0.002107 0.008631 0.001743 0.002438 0.003392
2 0.002839 0.010238 0.001909 0.003186 0.004508
3 0.002793 0.010509 0.001585 0.003610 0.004519
4 0.003137 0.010794 0.001569 0.003572 0.005168
5 0.003293 0.010699 0.001611 0.003504 0.005511
6 0.003476 0.010717 0.001286 0.003923 0.005946
7 0.003373 0.010630 0.001105 0.003980 0.005859
PSE 1 0.002720 0.002532 0.012534 0.002354 0.002792
2 0.004037 0.003496 0.013744 0.004353 0.004535
3 0.003873 0.003554 0.013203 0.004698 0.004649
4 0.004664 0.003473 0.012587 0.005219 0.005467
5 0.004522 0.003449 0.012576 0.005347 0.005576
6 0.004933 0.003293 0.011914 0.006350 0.005723
7 0.005139 0.003108 0.011751 0.006768 0.005631
SET 1 0.003410 0.003914 0.002602 0.013853 0.005060
2 0.003756 0.004555 0.003256 0.013892 0.005480
3 0.003845 0.004985 0.003782 0.014790 0.006311
4 0.004091 0.005459 0.004515 0.014805 0.006699
5 0.003881 0.004818 0.004692 0.014721 0.006660
6 0.003922 0.005431 0.004932 0.015535 0.007066
7 0.003364 0.004790 0.004150 0.014909 0.007037
STI 1 0.003667 0.004355 0.002469 0.004048 0.011083
2 0.003284 0.003814 0.002652 0.004329 0.011242
3 0.002991 0.003888 0.002978 0.004751 0.011037
4 0.003094 0.003998 0.002944 0.004987 0.011528
5 0.002847 0.004416 0.003423 0.005319 0.012042
6 0.002970 0.004458 0.003290 0.005579 0.012248
7 0.002747 0.004464 0.003485 0.005614 0.011606
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
8
As can be seen in Table 5, a generalised
impulse response analysis indicates that one
standard error shock to JAKCOMP would result in
a positive response by changes in STI of 0.0037,
one step ahead. Afterward, the responses have
become smaller ever since. A shock to STI,
commonly believed as the most prominent stock
index in ASEAN, results in second greatest
changes in the other indices in the short run
period. Meanwhile, the greatest contemporaneous
reaction of an index generally due to its own
shocks. This indicates that internal/domestic
shocks in a particular index may have greatest
significant impacts on its movements, and STI
become the most influential stock index in the
region at the time period.
While impulse response functions trace the
effects of a shock to one endogenous variable on to
the other variables in the system, variance
decomposition separates the variation in an
endogenous variable into the component shocks to
the system. As it is mentioned by Enders
(2004:280) the forecast error variance
decomposition tells the proportion of the
movements in a sequence due to its own shock
versus shock to the other variable. A shock to the ith
variable will not only affect that variable, but
can also be transmitted to all of the other variables
in the system.
Table 6 presents the result of the forecast error
variance decomposition of the serries in the period
of before financial crisis. As can be seen from the
table, in general, the proportion movements of the
indices are dominantly due to their own shocks.
Surprisingly, only around 70% of the error variance
of STI was attributable to own shocks in the steps
ahead, while JAKCOMP contributed maximum of
11% to STI’s error variance.
The Period of the 2007 Financial Crisis
The ADF test conducted to the serries at level
reveals the presence of unit root in the serries. The
lags order test then shows three lags length as the
appropriate lag order since the residual is not
serially correlated. However, the Johansen
Cointegration test fails to find the existence of
cointegration vector in the serries. This concludes
that the serries has no cointegrating relationship.
In other words, the indices have no long run
equilibrium during the 2007 financial crisis. The
finding somewhat contradicts with the ones given
by some other researchers (Arshanapalli et al 1993;
Sheng et al 2000, and Yang et al 2003), but
confirms that of Roca (2000:145).
The absence of cointegrating vector in the
series indicates that the cointegration analysis
framework is not possible to be carried out. Hence,
the VAR analysis framework would be applied to
estimate the relationship of the indices, as well as
to reveal the short run dynamic interactions among
the indices.
Table 6. Variance Decomposition
Variance Decomposition of JAKCOMP:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.012863 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.019020 99.85659 0.010939 0.028547 0.049753 0.054174
3 0.023434 99.50386 0.136144 0.109426 0.186402 0.064169
4 0.027354 98.70463 0.197381 0.416767 0.512016 0.169204
5 0.030979 97.84212 0.214017 0.842533 0.727739 0.373589
6 0.034447 96.85652 0.241250 1.088327 1.165416 0.648487
7 0.037525 95.98986 0.258696 1.232197 1.641083 0.878166
Variance Decomposition of KLSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.008631 5.960468 94.03953 0.000000 0.000000 0.000000
2 0.013407 6.954068 92.87426 0.028773 0.040597 0.102303
3 0.017064 6.971806 92.59963 0.141095 0.195879 0.091595
4 0.020238 7.359674 91.94159 0.229436 0.209507 0.259789
5 0.022955 7.778958 91.22487 0.270537 0.201897 0.523735
6 0.025444 8.198359 90.24934 0.397181 0.289759 0.865363
7 0.027681 8.411056 89.59566 0.531422 0.381012 1.080849
Variance Decomposition of PSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.012534 4.709320 2.360945 92.92974 0.000000 0.000000
2 0.018719 6.761489 2.970640 89.41751 0.670515 0.179843
3 0.023044 7.286824 3.322941 87.93643 1.180083 0.273723
4 0.026505 8.605063 3.336361 85.70276 1.768701 0.587110
5 0.029573 9.250590 3.348796 84.34696 2.224120 0.829537
6 0.032241 10.12412 3.263569 82.40609 3.231500 0.974721
7 0.034710 10.92704 3.118762 80.65164 4.273855 1.028701
Variance Decomposition of SET:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.013853 6.060589 5.260581 1.058338 87.62049 0.000000
2 0.019639 6.673187 6.267257 1.496936 85.55756 0.005057
3 0.024625 6.681866 6.857899 1.896486 84.48702 0.076732
4 0.028819 6.893286 7.554222 2.489728 82.92112 0.141642
5 0.032441 6.871721 7.475509 3.036180 82.40051 0.216083
6 0.036054 6.746606 7.689571 3.399525 81.89916 0.265139
7 0.039080 6.483311 7.641728 3.442428 82.03551 0.397027
Variance Decomposition of STI:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.011083 10.94870 10.36302 1.108366 4.525942 73.05397
2 0.015812 9.692145 8.950799 1.464531 5.434735 74.45779
3 0.019334 8.876028 8.824737 1.942618 6.518288 73.83833
4 0.022557 8.401438 8.680124 2.081363 7.188879 73.64820
5 0.025643 7.733370 8.956077 2.414139 7.690111 73.20630
6 0.028485 7.354068 9.084298 2.515628 8.205909 72.84010
7 0.030842 7.066422 9.358043 2.738126 8.729031 72.10838
The VAR analysis, however, requires that the
series must be stationary. Hence, the non
stationary series may be made stationary by
differencing or detrending process. After
transforming the serries into first difference form,
the ADF test is re-employed to ensure that the
series are now stationary. The lag order test then
indicated that the appropriate lag length would be
three. After estimating the series using the VAR in
first difference analysis, the estimated models can
be shown in Table 7.
Table 8 shows the results of a block causality
test implemented on the series. The table reveals
that, using a 5 % level of significance, more
variables significantly granger cause another in the
crisis period compared to those in pre crisis period.
It means that there are more variables that their
current values have correlation with the past (lags)
value of another implying that the present value of
an index can be predicted with greater accuracy by
using past value of another. This then indicates
that there is an increase in causal linkages among
Atmadja: The Asean Stock Market Integration
9
those indices in the region during the crisis period.
The results are in fact different with those before
the crisis period showing a changing behaviour in
the indices’ movements. For instance, the lags of
SET and STI now significantly enter into the
equation for JAKCOMP, while in the pre crisis
period does not.
Table 7. Vector Autoregression Estimates
JAKCOMP KLSE PSE SET STI
JAKCOMP(-1) 0.072296 0.134318 0.132358 0.101597 0.029438
(0.06530) (0.03643) (0.05607) (0.05610) (0.06375)
[ 1.10716] [ 3.68735] [ 2.36041] [ 1.81101] [ 0.46179]
JAKCOMP(-2) 0.120791 0.083802 0.143389 0.198738 0.096179
(0.06664) (0.03718) (0.05723) (0.05726) (0.06506)
[ 1.81247] [ 2.25413] [ 2.50552] [ 3.47108] [ 1.47829]
JAKCOMP(-3) -0.058141 -0.010836 -0.017686 -0.026591 0.005682
(0.06641) (0.03705) (0.05703) (0.05706) (0.06484)
[-0.87543] [-0.29248] [-0.31011] [-0.46604] [ 0.08764]
KLSE(-1) -0.208902 -0.185895 -0.070152 -0.186469 -0.231196
(0.10815) (0.06033) (0.09287) (0.09291) (0.10558)
[-1.93162] [-3.08130] [-0.75537] [-2.00693] [-2.18980]
KLSE(-2) -0.210401 -0.180766 -0.102958 -0.375966 -0.275983
(0.10879) (0.06069) (0.09342) (0.09346) (0.10620)
[-1.93407] [-2.97869] [-1.10212] [-4.02270] [-2.59867]
KLSE(-3) 0.062985 0.124513 0.038155 0.027387 0.117089
(0.10840) (0.06047) (0.09308) (0.09313) (0.10582)
[ 0.58106] [ 2.05914] [ 0.40990] [ 0.29409] [ 1.10649]
PSE(-1) 0.017456 0.029010 -0.043004 0.048177 0.012092
(0.06255) (0.03489) (0.05371) (0.05374) (0.06106)
[ 0.27907] [ 0.83140] [-0.80062] [ 0.89653] [ 0.19802]
PSE(-2) 0.051068 0.048802 0.014394 0.077747 0.058784
(0.06224) (0.03472) (0.05345) (0.05347) (0.06076)
[ 0.82050] [ 1.40558] [ 0.26931] [ 1.45401] [ 0.96747]
PSE(-3) -0.020801 -0.019708 -0.040438 0.010962 -0.049437
(0.06019) (0.03358) (0.05168) (0.05171) (0.05876)
[-0.34561] [-0.58698] [-0.78239] [ 0.21200] [-0.84138]
SET(-1) 0.135694 -0.014998 0.029879 -0.069210 -0.078839
(0.07317) (0.04082) (0.06283) (0.06286) (0.07143)
[ 1.85453] [-0.36744] [ 0.47554] [-1.10100] [-1.10371]
SET(-2) -0.043142 0.019411 -0.125749 0.016529 0.040208
(0.07254) (0.04047) (0.06229) (0.06232) (0.07082)
[-0.59473] [ 0.47969] [-2.01868] [ 0.26523] [ 0.56778]
SET(-3) -0.149188 -0.042758 -0.077355 -0.060061 -0.147843
(0.07241) (0.04039) (0.06218) (0.06221) (0.07069)
[-2.06042] [-1.05858] [-1.24410] [-0.96552] [-2.09154]
STI(-1) 0.151469 0.077991 0.230158 0.060714 0.124110
(0.07280) (0.04061) (0.06252) (0.06255) (0.07107)
[ 2.08051] [ 1.92034] [ 3.68142] [ 0.97068] [ 1.74621]
STI(-2) 0.048648 0.026292 0.062191 0.044742 0.028764
(0.07342) (0.04096) (0.06305) (0.06308) (0.07168)
[ 0.66258] [ 0.64191] [ 0.98637] [ 0.70931] [ 0.40130]
STI(-3) 0.155315 -0.012852 0.046721 0.128301 0.021717
(0.07118) (0.03971) (0.06113) (0.06116) (0.06949)
[ 2.18190] [-0.32366] [ 0.76432] [ 2.09796] [ 0.31251]
C -0.000179 -0.000638 -0.000853 -0.000801 -0.001001
(0.00093) (0.00052) (0.00080) (0.00080) (0.00091)
[-0.19335] [-1.23409] [-1.07116] [-1.00633] [-1.10644]
Note: Standard errors in ( ) & t-statistics in [ ]
5 % level of significant
Table 8. VAR Pairwise Granger Causality/Block
Exogeneity Wald Tests
Dependent
variable Exclude Chi-sq Prob.
JAKCOMP KLSE 7.633367 0.0542
PSE 0.892896 0.8271
SET 7.966430 0.0467
STI 9.699188 0.0213
KLSE JAKCOMP 18.50092 0.0003
PSE 3.030814 0.3869
SET 1.542379 0.6725
STI 4.229433 0.2377
PSE JAKCOMP 11.80429 0.0081
KLSE 1.972551 0.5781
SET 5.832183 0.1201
STI 15.30169 0.0016
SET JAKCOMP 15.51245 0.0014
KLSE 19.47387 0.0002
PSE 2.766382 0.4291
STI 5.971454 0.1130
STI JAKCOMP 2.368227 0.4996
KLSE 12.88606 0.0049
PSE 1.790673 0.6170
SET 6.127043 0.1056
In order to capture the short run dynamic
interaction among the variables during the
financial crisis period, the generalized impulse
response and the forecast error variance
decomposition, would also be employed. The results
of the generalized impulse response analysis of the
series are presented in Table 9. As it is shown in
the table, during the financial crisis, the
generalised impulse response analysis indicates
that all variables gave greater immediate reactions
to a shock of one variable compared to those in the
pre-crisis era. This implies that the short run
interaction between two indices became more
intense during the 2007 financial crisis period. In
other words, the findings strongly indicate that the
ASEAN indices become more interdependent
during the financial crisis, although they had no
long run equilibrium.
The variance decomposition analysis (Tabel
10) reveals that the proportion of the movements in
an index due to its own shock for all indices
declined during the financial crisis. This means
that in the period of the financial crisis shocks to
other indices have more explanatory power to the
movements of a particular index in the s-steps
ahead. This finding seems reinforce the result of
generalized impulse response analysis that during
the 2007 financial crisis period, the ASEAN’s stock
indices tend to be more interdependent. Thus, it
somewhat confirmed the previous researches done
by Roca (2000:145) and Yang et al (2003:478)
which conclude that interdependency and causal
linkages among the indices become more
significant during crisis.
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
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Table 9. The Impulse Response to Generalized
One S.D. Innovations
Response
of
Period JAKCOMP KLSE PSE SET STI
JAKCOMP 1 0.020461 0.011579 0.008953 0.011621 0.013632
2 0.003635 0.001526 0.002198 0.004214 0.004293
3 0.001716 -0.000584 0.000966 0.000523 0.001221
4 0.000100 0.000434 -0.000181 -0.000541 0.001408
5 -0.000507 -0.000267 -0.000492 -0.000799 -0.000178
6 -0.000571 -0.000327 -0.000373 -0.000433 -0.000329
7 -0.000532 -0.000227 -0.000441 -0.000583 -0.000670
KLSE 1 0.006459 0.011414 0.005095 0.005452 0.006781
2 0.002659 0.000461 0.001297 0.001534 0.002167
3 0.001598 -0.000120 0.001140 0.001414 0.001395
4 -4.01E-05 0.000663 -0.000214 -0.000399 5.15E-05
5 8.40E-05 -0.000142 -0.000101 -0.000276 0.000126
6 -0.000133 -7.84E-05 -8.03E-05 -7.53E-05 8.39E-05
7 -0.000223 -3.72E-05 -0.000149 -0.000202 -0.000250
PSE 1 0.007689 0.007843 0.017570 0.007738 0.007307
2 0.005286 0.003377 0.002215 0.004376 0.005958
3 0.002075 -2.87E-05 0.000618 -4.34E-05 0.001447
4 -0.000102 -0.000320 -0.000544 -0.000358 0.000359
5 -0.000711 -0.000156 -0.000596 -0.000995 -0.000434
6 -0.000525 -0.000297 -0.000408 -0.000535 -0.000410
7 -0.000345 -0.000107 -0.000214 -0.000261 -0.000347
SET 1 0.009984 0.008397 0.007742 0.017578 0.011578
2 0.001362 -0.000435 0.000775 0.000119 0.000884
3 0.003034 -0.000533 0.001747 0.002050 0.002101
4 0.000804 0.001367 0.000655 0.000834 0.002134
5 -0.000160 -0.000455 -0.000346 -0.000645 -0.000140
6 -0.000169 -0.000252 -0.000108 -0.000116 0.000174
7 -0.000474 -0.000120 -0.000405 -0.000544 -0.000503
STI 1 0.013308 0.011867 0.008307 0.013156 0.019974
2 6.65E-05 -0.001392 -0.000281 -0.000578 0.000488
3 0.000879 -0.001056 0.000734 0.000894 0.000598
4 -0.001135 0.000113 -0.001453 -0.002127 -0.000913
5 -0.000329 -0.000385 -0.000354 -0.000535 -0.000465
6 -0.000392 -5.06E-05 -0.000155 -9.08E-05 -8.33E-05
7 -0.000300 -0.000110 -0.000214 -0.000306 -0.000493
Table 10. Variance Decomposition
Variance Decomposition of JAKCOMP:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.020461 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.021073 97.24338 0.093183 0.174688 1.656038 0.832708
3 0.021264 96.15798 0.878554 0.303776 1.644745 1.014946
4 0.021412 94.83229 0.912108 0.332163 1.758313 2.165129
5 0.021436 94.68301 0.910264 0.353508 1.830443 2.222772
6 0.021445 94.67600 0.909518 0.357529 1.831413 2.225544
7 0.021458 94.61447 0.910093 0.372423 1.851690 2.251322
Variance Decomposition of KLSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.011414 32.02605 67.97395 0.000000 0.000000 0.000000
2 0.011842 34.79170 64.28911 0.182911 0.037178 0.699104
3 0.012084 35.16451 62.80328 0.676161 0.434642 0.921404
4 0.012135 34.86724 62.73966 0.813231 0.661356 0.918514
5 0.012148 34.79750 62.64135 0.817565 0.737312 1.006279
6 0.012152 34.78759 62.60206 0.817460 0.736878 1.056009
7 0.012156 34.79723 62.56663 0.822289 0.743584 1.070267
Variance Decomposition of PSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.017570 19.14909 5.811252 75.03966 0.000000 0.000000
2 0.018664 24.99113 5.212981 66.52072 0.824013 2.451155
3 0.018915 25.53371 5.670186 64.76357 1.220127 2.812400
4 0.018949 25.44521 5.678093 64.59816 1.227054 3.051487
5 0.018986 25.48622 5.680653 64.39330 1.373668 3.066158
6 0.018997 25.53520 5.674576 64.33616 1.390703 3.063358
7 0.019001 25.55543 5.674949 64.30681 1.392098 3.070709
Variance Decomposition of SET:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.017578 32.25784 3.592081 2.848586 61.30150 0.000000
2 0.017729 32.29985 4.211044 2.919796 60.38029 0.189017
3 0.018269 33.17686 6.197357 3.213120 57.06075 0.351916
4 0.018426 32.80436 6.452377 3.159159 56.10525 1.478857
5 0.018446 32.74148 6.496111 3.163665 56.07662 1.522124
6 0.018455 32.71765 6.500189 3.160603 56.02145 1.600115
7 0.018468 32.73729 6.500537 3.178835 55.97476 1.608586
Variance Decomposition of DLNSTI:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.019974 44.38852 6.931322 0.493399 7.788191 40.39857
2 0.020116 43.76814 7.577686 0.490308 7.717361 40.44651
3 0.020262 43.32822 8.333029 0.692744 7.747659 39.89834
4 0.020443 42.86986 8.386302 1.122186 8.404362 39.21729
5 0.020451 42.86178 8.393528 1.128666 8.426711 39.18932
6 0.020457 42.87448 8.399131 1.128383 8.425800 39.17221
7 0.020464 42.86514 8.394334 1.130788 8.426286 39.18345
CONCLUSION
The study concludes that two cointegrating
vectors are found in the series before the 2007
financial crisis period indicating the existing of long
run equilibrium in the series during the time
period. However, the study fails to find any
cointegrating vector in the series during the
financial crisis period. The results prove that the
long run relationship of the ASEAN indices has
been removed by the 2007 financial crisis.
The block causality tests employed in both subsample
period reveal that more significant causal
linkages are found in the series during the
financial crisis period compared to those before the
financial crisis. The accounting innovation
analyses conducted to the series also indicate that
the short run dynamic interactions among the
indices tend to be more intense during the financial
crisis period. These all indicate that the indices
become more interdependent during the financial
crisis period since the moment gives rise the
explanatory power of a sequence to the movements
of another.
The general conclusion that may be withdrawn
from this study is that the contagious effect of the
2007-US financial crisis has affected the ASEAN’s
capital market integration, and has changed the
behaviour of the indices’ movements both in the
short run and in the long run.
Thus, the implication policy that can be
suggested is that the diversification of portfolio
within the ASEAN stock markets in the short run
is unlikely to reduce the risk due to the high degree
of financial interdependent of these markets
during the financial crisis.
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Vancouver, http://www.cfapubs.org.

Jurnal Akuntansi-B.Inggris

Mei 12, 2010

The Asean Stock Market Integration:
The Effect of the 2007 Financial Crisis on the Asean Stock Indices’
Movements
Adwin Surja Atmadja
Faculty of Economics, Petra Christian University
E-mail: aplin@peter.petra.ac.id
ABSTRACT
This study attempts to examine the existence of cointegration relationship and the short
run dynamic interaction among the five ASEAN stock market indices in the period of before
and during the 2007 financial crisis. The multivariate time series analysis frameworks are
employed to the series in both sub-sample periods in order to answer the hypotheses.The
study finds two cointegrating vectors in the series before the financial crisis period, however
it fails to detect any cointegrating vector in the period of financial crisis. Granger causality
tests applied to the series reveal that number of significant causal linkages between two
variables increase during the crisis period. Moreover, the accounting innovation analysis
shows an increase in the explanatory power of an endogenous variable to another within the
system during the crisis period, indicating that the contagious effect of the 2007-US financial
crisis has entered into the ASEAN capital market, and significantly influenced the regional
indices’ movements.
Keywords: ASEAN, stock market integration, the 2007 financial crisis, regional indices’
movements.
INTRODUCTION
Liberalization of the five ASEAN (Indonesia,
Malaysia, the Philippines, Singapore, and
Thailand) financial markets in 1980s resulted in
enormous capital inflows to this region. By opening
their national borders for foreign investors, the
countries’ financial markets were overwhelmed by
foreign capital in both foreign direct and portfolio
investments giving significant support to their
rapid domestic economic development, as well as
enjoyed rapid financial markets expansion in the
beginning of 1990s. Capital inflows have been
crucial to the rapid – sustained growth in ASEAN
countries (Sachs and Larrain, 1993:577) at that
time, since domestic saving, as commonly in
developing countries, had little role as development
funding.
Triggered by the sharp depreciation of the
Thai baht in the midst of 1997, the disastrous
effects of the 1997 financial crisis were broadly
spread out to the countries’ financial markets
which were dominated by bank loan and portfolio
investment, not by foreign direct investment
(DFAT, 1999:29). The crisis then extensively
affected the world financial markets through its
contagion effects. Market capitalization of the
countries’ stock market was largely contracted due
to a deep depreciation in their stock prices causing
their stock indices then sharply plunged.
However, the downturn in the five ASEAN
rebounded in 1999. After the sharp output
contraction in 1998, growth returned in that year
as depreciated currencies spurred higher exports
(Krugman and Obstfeld, 2003:693). Following the
appreciation of regional currencies in the second
semester of the year, the regional capital and
financial markets started to recover. The regional
stock market indices increased around 42.46% on
average compared to those from two years before
(calculated from IFS 2004). This might indicate
that investors’ confidence started to recover and
they began to invest in the five ASEAN.
During ten years after, the ASEAN’s
economies steadily grew to their new equilibrium.
As a market indicator, the ASEAN capital market
indices apparently fluctuated in a relatively narrow
range dominantly due to small internal shocks in
the short run, but stably moved with positive
trends in the long run. These all mirror that the
ASEAN markets were relatively stable during the
time periods, and their economies were just on the
right tracks.
However, in the second semester of 2007 the
countries experienced significant shocks in their
capital markets due to a contagious effect of the US
financial market turmoil. At the time, the US
financial market deeply suffered from the most
1
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significant economic shocks initiated by the subprime
mortgage crisis leading to the downturn in
housing market, and then worsened by the spike in
commodity prices (Yellen 2008:1). The devastating
effects of the 2007 financial crisis in the US then
widely spread throughout the world.
From the facts above, the 2007 financial crisis
may have significant consequences on the variation
of the countries’ stock indices that probably
different with those in non crisis era. The financial
crisis could possibly cause the regional indices
deviate from their long run equilibrium, and the
behaviour of the indices’ movements may be
different with those before. All possibilities may
happen in the regional market depended on how
significant the impact of the financial crisis hit the
market. Therefore, this study will empirically
examine how the 2007 financial crisis has taken
into effect on the five ASEAN stock indices’
movements. To be more specific, this study
attempts to observe the existing of cointegrating
relationships among the five ASEAN stock indices
in the periods of before (pre) and during the 2007
financial crisis in order to portray the long run
interrelations among the indices in the both
periods. The aim is also to answer how and to what
extent the stock indices dynamically interact with
each other in the short run during the given
periods.
CONCEPT OF FINANCIAL MARKET OR
STOCK MARKET INTEGRATION
The basic theoretical concept of financial
market or stock market integration is adopted from
the law of one price. In integrated financial
markets, the assets with the same risk in different
markets will result in the same yield when
measured in a common currency (Stulz 1981:924-
5). However, if the yields are different across the
markets, the arbitrage process will play an
important role in eliminating the differences.
Operationally capital markets integration refers to
the extent that markets’ participants are enabled
and obligated to take notice of events occurring in
other markets by using all available information
and opportunities, while financial market
integration is defined in terms of price
interdependence between markets (Kenen 1976:9).
Moreover, stock market integration is affected by
some factors (Roca 2000:14), such as:
1. Economic integration, which means that the
more integrated the economies of countries, the
more integrated their equity markets (Eun and
Shim 1989: 256).
2. Multiple listing of stocks. This implies that a
shock in a particular stock market can be
transmitted to other stock market through
shares listed in both markets.
3. Regulatory and information barriers. The
higher the barriers, the lower the degree of
stock market integration.
4. Institutionalisation and securitisation. As
institutions are more willing to transfer funds
overseas to increase their diversification
opportunities, the integration will be promoted.
5. Market contagion. The prices between stock
markets can move together due to a contagion
effect (King and Wadwhani 1990:5), and this
contagion effect determines significantly the
dynamic relationships between international
stock markets (Climent and Meneu, 2003:111).
However, in emerging stock markets, this effect
might be smaller than what is widely perceived
(Pretorius 2002:103).
Much research has been done, mainly by using
a cointegration analytical framework, to find and
analyse the existence of integration in stock
market across countries. The results are different
depending on where, when, and how the research
has being conducted. The cointegration analytical
framework has been widely applied to examine the
integration of stock markets across countries. Once
a cointegration vector is found among two or more
stock markets, it indicates the existence of a long
run relationship among them. Thus, stock price
movements in one equity market will affect
another in other markets.
A research conducted by Chung and Liu
(1994:55) found two cointegration vectors between
the U.S and larger Asia Pacific stock markets.
Palac-McMiken (1997:299) also reveals the
existence of cointegration in ASEAN markets
(Malaysia, Singapore, Thailand, and the
Philippines), except Indonesia, during 1987 to
1995. Both results were confirmed by Masih and
Masih (1999:275) who report that some of ASEAN
countries (Thailand, Malaysia, and Singapore)
have a high degree of interdependence with other
Asian (Hong Kong and Japan) and developed (the
U.S. and the U.K.) stock markets. Furthermore,
they also find one cointegration vector among
several major Asian stock markets (Hong Kong,
Korea, Singapore, and Taiwan) and major
developed markets (Masih and Masih 2001: 580-1).
Interestingly, Pretorius (2002:103) reports that
the degree of bilateral trade and the industrial
production growth differential significantly
explained the correlation between two equity
markets, and that the stock markets of countries in
the same region are more interdependent than
those in different regions. Consistent with this
finding, Roca (2000:145) finds the existence of
Atmadja: The Asean Stock Market Integration
3
interdependency among all the ASEAN stock
markets in the short run. However, in contrast to
short run interdependency, he indicates that there
was no cointegration among ASEAN countries as a
group during 1988-1995 and that those stock
markets were not significantly related to each
other in the long run.
Chan, Gup and Pan (1992:289) and DeFusco,
Geppert and Tsetsekos (1996:343) also mention
that there is no cointegration between the U.S and
several Asian emerging stock markets (Hong Kong,
Taiwan, Singapore, Korea, Malaysia, Thailand,
and the Philippines) in the 1980s and early 1990s.
However, these findings somewhat contradicts
with those of Chung et al. (1994) and Masih et al.
(1999). This then implies that the interdependence
among stock markets is not stable over time. For
example, Hung and Cheung (1995:286) assert that
there is no cointegration among stock markets in
some Asia-Pacific countries (Malaysia, Hong Kong,
Korea, Singapore, and Taiwan). However, when
they used US dollar denominated stock prices, it
was reported that those stock markets were
cointegrated after, but not before, the 1987 stock
crash.
Arshanapalli and Doukas (1993:206) also
mention the instability of stock market
interdependence when they tested the effect of
inclusion or omission of the data for the 1987 crisis
and revealed that that it affects the results. They
conclude that the stock markets were highly
integrated during the crisis. Furthermore,
Arshanapalli, Doukas and Lang (1995:72) show
that after the 1987 crisis the stock markets in
emerging markets (Malaysia, the Philippines, and
Thailand) and developed markets (Hong Kong,
Singapore, the U.S., and Japan) are more
interdependent as they found cointegration in the
post-crisis period, but not in the pre-crisis period.
Other researchers, Liu, Pan and Shieh (1998: 59)
also confirm that there is an increase in the
interdependence within Asian-Pacific regional
markets and the stock markets in general post-the
1987 crisis. Similarly, Sheng and Tu (2000:245)
document one cointegration vector between the
U.S. and several Asian stock markets (Taiwan,
Malaysia, China, Thailand, Indonesia, South
Korea, the Philippines, Australia, Japan, Hong
Kong, and Singapore) during the crisis, but none in
the year before the crisis, when they observed the
stock markets using daily data.
Finally, a research recently conducted by
Yang, Kolari and Min (2003:478) examined the
long-run relationship and short-run dynamic
causal linkages among the U.S, Japanese, and ten
Asian emerging markets using daily data of 1997-
1998 periods. They confirm that the stock markets
of those countries have been more integrated after
the 1997 Asian financial crisis than before the
crisis. Both long-run cointegration relationship and
short-run causal linkages among those markets
become more significant during the crisis. These
findings also confirm that the degree of integration
among those countries tends to change over time.
Several points that may be drawn form the
literature review. The implication is that
liberalization of the financial sector in many
countries has caused world or regional stock
markets to be more integrated. Empirical evidence
is given by the presence of cointegration vectors
and significant short-run causal linkages. It is
worth noting that the stock markets of countries in
the same region may be more interdependent than
those in different regions.
RESEARCH METHODOLOGY
Basically, a stock market price index or stock
market index is a portfolio of individual stocks. The
index level corresponds to some average of the
price levels of individual shares. Changes in the
index level give rise to market returns. Thus, the
stock market index, which can be viewed simply as
a portfolio of shares, can commonly be use as an
indicator of the market performance. There are
several factors that determine the level of the
index, such as breadth of index, weighting system,
capitalization adjustment, and dividend effect
(Brailsford Heaney and Bilson 2004:68).
The stock market index of a country may also
be an indicator of short-term portfolio investment
movement in the country. An upward trend of a
stock market index means that there is an increase
in demand of the listed shares in the market. This
indicated that investors are attracted to buy shares
and invest their fund in the country. On the other
hand, a downward trend movement of a stock
market index indicates that the investors are
unlikely to continuously hold the listed shares.
Hence, stock market movements may reflect the
attractiveness of a country for investments,
especially for portfolio investments.
In this study, the daily closing stock price
indices of the five ASEAN countries, which are
Jakcomp of Indonesia; KLSE of Malaysia; PSEi of
the Philippines; STI of Singapore; and SET
Composite of Thailand, are employed as
measurement of the countries’ daily stock index
movements in the periods of before and during the
2007 financial crisis.
Some previous research (Arshanapalli et al,
1993, Chung et al, 1994, Arshanapalli et al, 1995,
Liu et al,1998, Masih et al., 1999, Masih et al,
2001) document that stock markets in the Asian
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region are interdependent not only among
themselves, but also with some of the developed
market. Furthermore, those stock markets are
even more interdependent during and after the
financial crisis (Sheng et al 2000; Yang et al 2003)
In the case of the ASEAN, Palac-McMiken
(1997:299) reports the existence of cointegration in
the countries’ stock markets, except Indonesia,
before the 1997 crisis. Yang et al (2003:478)
confirm that both long-run cointegration
relationship and short-run causal linkages among
those markets become more significant during the
crisis period. In contrast, Roca (2000:145) finds the
existence of interdependency among the five
ASEAN’s stock markets in the short run, but not
significantly related in the long run before the 1997
crisis.
Based on these findings, it is hypothesized that
the ASEAN stock indices would have long run
cointegration relationship and short run dynamic
interaction, and that the relationship and the
interaction would be more significant during the
2007 financial crisis.
All daily price index data of the five ASEAN
during the observation periods are obtained from
the Thomson Financial. The index data of all
variables then will be transformed into natural
logarithm forms before conducting the analyses.
In order to examine the movements of the
indices in both periods, the data are then separated
into two sub-sample periods, which are the periods
of: 1) Before the 2007 financial crisis (pre crisis),
which cover the period of Jan 2000 – June 2007, 2)
During the 2007 financial crisis, which cover the
period of July 2007 – May 2009, as it is stated in
several publications (http://en.wikipedia.org,www.
globalissues.org,www.atypon-link.com)
The two most appropriate models that one of
which may suitable for this study are VAR and
VECM. In the Vector autoregressive model (VAR)
all of the variables are endogenous, and
symmetrically treated. A VAR could be very large,
however the simplest VAR model, in standard
form, could be written as (Enders, 2004:265):
Yt = a10 + a11Yt-1 + a12 Zt-1 + eYt.
Zt = a20 + a21Yt-1 + a22 Zt-1 + εZt.
The VAR requires that all variables be
stationary and the appropriate lag length is data
driven (Brooks 2002:333). There are several
available tests for testing for a unit root, the most
common is the Augmented Dicky-Fuller (ADF)
test. Non-stationary variables may be made
stationary by differencing or detrending process.
To define the appropriate lag length, some
tests of information criteria that will be applied in
this study include the likelihood ratio test; Akaike
Information Criterion (AIC); and Schwarz
Bayesian Criterion (SC).
The likelihood ratio test is based on asymptotic
theory and is an F-type approximation. This test
actually compares a restricted VAR (less lags) to an
unrestricted VAR (more lags). Thus, the null
hypothesis of this test is that the restricted model
is correct. However, the shortcoming of this test is
that it may not be useful in small samples. In
addition, the likelihood ratio test is only valid when
the restricted model is tested (Enders 2004:283).
Because of the limitations of the likelihood
ratio test, multivariate generalization of AIC and
SC may be the most suitable alternatives. The
minimum values of AIC and/or SC may validly
indicate the appropriate lags length, as long as the
model’s residual has no serial correlation problem.
Otherwise, the lag length may be too short. Thus, it
is necessary to re-estimate the model using lag
length that yield serially uncorrelated (Enders
2004:338).
In VAR, a block causality test will be used to
examine whether the lags of one variable enter into
the equation for another variable (Enders
2004:283). A variable (y1) is said to be a grangercause
of another (y2) if the present value of y2 can
be predicted with greater accuracy by using past
values of y1, all other information being identical
(Thomas 1997:461). If y1 granger-causes y2, then
the parameters of lags of y1, βi’s, should not equal
zero in the equation of y2. However, it is worth
noting that granger-causality basically means a
correlation between the current value of one
variable and the past (lags) value of others. It does
not mean that movements of one variable
physically cause movements of another (Brooks,
2002:240). Granger causality simply implies a
chronological ordering of movements of the series.
Therefore, it could validly be stated that changes or
movements in one variable (y2) appear to lag those
of another (y1).
The alternative model that probably suitable
to be used is the vector error correction model
(VECM) or cointegration framework analysis,
which is basically is a VAR augmented by the error
correction term (êt-1). The simplest VECM, in
general, takes the form as (Enders 2004:329):
ΔYt = α10 + αY êt-1+ Σ α11(i) ΔYt-i + Σ α12(i) ΔZt-i + εYt.
ΔZt = α20 + αZ êt-1+ Σ α21(i) ΔYt-i + Σ α22(i) ΔZt-i + εYt.
where
êt-1 = (Yt-1 – β1Z1t-1)
Thus, if the parameters of error correction
term (ECT), called speed of adjustments (αY and αZ)
in VECM, are zero, then VECM reverts to a VAR
in first differences (Enders 2004:329).
ΔYt = α10 + Σ α11(i) ΔYt-i + Σ α12(i) ΔZt-i + εYt.
ΔZt = α20 + Σ α21(i) ΔYt-i + Σ α22(i) ΔZt-i + εYt.
However, if the speed of adjustments are not
zero, the larger the speed of adjustments, the
Atmadja: The Asean Stock Market Integration
5
greater the response to previous periods’ deviation
from the long run equilibrium. Thus, a
cointegration relationship is a long term or
equilibrium phenomenon, since it is possible that
cointegrating variables may deviate from their
relationship in the short run, but their association
would return in the long run. A principal feature of
cointegrated variable is that their time paths are
influenced by the extent of any deviation from long run
equilibrium. After all, if the system is to return to long
run equilibrium, the movements of at least some of the
variables must respond to the magnitude of the
disequilibrium. (Enders 2004:328). The VECM result is
also sensitive to its lags length. Thus, it is essential
to use appropriate lag length to get the appropriate
outcomes by conducting the lag order selection
criteria (LR, AIC, or SC) tests.
Unlike VAR, cointegration refers to a linear
combination of non-stationary variables. Thus, it is
necessary to test the existence of unit roots in
observed variables using the ADF test as it is used
in VAR. Cointegration also requires that all
variables in a model be integrated of the same
order. Thus, in order to test the existence of
cointegrated variable, one may use the Engle-
Granger (EG) test, which is a residuals-based
approach, or the Johansen Cointegration test. In
the case of a cointegration relationship does not
exist, a VAR analysis in first difference will then be
the correct specification to conduct the estimation
(Enders, 2004:287).
After estimating the VECM equations, the
VEC Pairwise Granger Causality / Block Exogenity
Wald Tests will be applied to reveal whether
changes in one variable cause changes in another.
If so, then lags of variable should be significant in
the equation for the other variable. If this is the
case, it can be said that the variable grangercauses
another.
A direct interpretation of the cointegration
relations may be difficult or misleading (Lutkepohl
and Reimers 1992:53, Runkle 1987:442). As in a
traditional VAR analysis, innovation accounting,
consist of Impulse Response and Variance
Decomposition Analyses, can provide a solution to
the interpretation problem, and might be the most
appropriate method to explain the short run
dynamic structure of market linkages (Yang et al
2003:479). The analysis would give to answers
whether changes in the value of a given variable
have positive or negative effect on other variables
in the system, or how long it would take for the
effect of that variable to work through the system
(Brooks 2002:341).
A shock to the i-th variable not only directly
affects the i-th variable but is also transmitted to
all of the other endogenous variables through the
dynamic (lag) structure of the VAR. An impulse
response function traces the effect of a one-time
shock to one of the innovations on current and
future values of the endogenous variables. In other
words, impulse response analysis will trace out the
responsiveness of the dependent variables in VAR
to shocks on individual error terms. In this paper,
the generalized type of impulse responses analysis
is employed as orthogonalized impulse responses is
sensitive to the ordering of the variable in the
system. The Generalized Impulses as described by
Pesaran and Shin (1998) constructs an orthogonal
set of innovations that does not depend on the VAR
ordering. The generalized impulse responses from
an innovation to the j-th variable are derived by
applying a variable specific Cholesky factor
computed with the j-th variable at the top of the
Cholesky ordering. Dekker, Sen and Young
(2001:31) found that the generalized approach
provided more accurate results than the traditional
orthogonalized approach for both impulse response
and forecast error variance decomposition analysis
Forecast error variance decomposition,
meanwhile, refers to the proportion of the
movements in a sequence due to its own shock
versus shocks to the other variables (Enders
2004:280). This analysis separates the variation in
an endogenous variable into the component shocks
to the system. Thus, the variance decomposition
provides information about the relative importance
of each random innovation in affecting the
variables in the system. It determines how much of
the s-step ahead forecast error variance of a given
variable is explained by innovations to each
explanatory variable. A shock to the i-th variable
will not only affect that variable, but also can be
transmitted to all of the other variables in the
system. To some extent, impulse responses and
variance decompositions offer very similar
information.
EMPIRICAL RESULTS
The Period of before the 2007 Financial
Crisis
The ADF test applied to all variables at level
within the sub-sample period results in acceptence
(fail to reject) of the null hypothesis that the serries
contain unit root. The existence of a unit root in
Asian stock markets, including the ASEAN is well
established in the literature (Masih et al 1999,
2001). The examination then continues to select
the appropriate lag order. The lag orders suggested
by the three lag order selection criteria result in
serially correlated residual. Therefore, as
mentioned by Enders (2004:338), it is necessary to
re-estimate the model using all possible lag length
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
6
until the residual is found serially uncorrelated.
After examination of all possible lag length, the
appropriate lag length is found to be six.
The Johansen Cointegration test then reveals
that there are conflicting results between max
and trace statistic as it is stated in Table 1.
However, as it is suggested by some
econometricians (Johansen and Juselius 1990;
Kasa, 1992; and Serletis and King 1997) that the
trace tends to have more power than the max
because trace takes into account all degrees of
freedom (n-r) of the smallest eigenvalues, then the
number of cointegration vectors suggested by the
trace statistic would be employed. Thus, it may
be concluded that there are two cointegrating
vectors found in the series of the sub-sample period
at 5% level of significance, meaning that the
ASEAN indices are highly interdependent and
significantly related to each other in the long run
during the pre crisis period.
Table 1. The Johansen Cointegration Test For the
sub-sample period of before the 2007
financial Crisis
Trend assumption: Linear deterministic trend
Unrestricted Cointegration Rank Test
Hypothesized
No. of CE(s)
Eigenvalue Trace
Statistic
5 Percent
Critical
Value
1 Percent
Critical
Value
None ** 0.017748 83.02299 68.52 76.07
At most 1 * 0.010864 48.13862 47.21 54.46
At most 2 0.008270 26.85945 29.68 35.65
At most 3 0.004107 10.68322 15.41 20.04
At most 4 0.001368 2.667069 3.76 6.65
Trace test indicates 2 cointegrating equation(s) at the 5% level
Trace test indicates 1 cointegrating equation(s) at the 1% level
Hypothesized
No. of CE(s)
Eigenvalue Max-
Eigen
Statistic
5 Percent
Critical
Value
1 Percent
Critical
Value
None * 0.017748 34.88437 33.46 38.77
At most 1 0.010864 21.27917 27.07 32.24
At most 2 0.008270 16.17623 20.97 25.52
At most 3 0.004107 8.016154 14.07 18.63
At most 4 0.001368 2.667069 3.76 6.65
Max-eigenvalue test indicates 1 cointegrating equation(s) at the
5% level
Max-eigenvalue test indicates no cointegration at the 1% level
The existence of cointegrating vectors resulted
from this study is somewhat consistent with
previous research conducted by Palac-McMiken
(1997:299) and Liu et al (1998:59), but contradicts
with that of Sheng et al (2000:245), in different
period of time. Thus, it can be argued that VECM
is possible to be carried out to estimate the stock
indices of the five ASEAN.
The results of the VECM estimation can be
shown in the two consecutive tables. Table 2
(APPENDIX) shows the estimated cointegrating
vectors, whereas Table 3 report the coefficient of
speed of adjustment.
Table 2. Estimated Cointegrating Vectors
Cointegrating Eq: CointEq1 CointEq2
JAKCOMP 1.000000 0.000000
KLSE 0.000000 1.000000
PSE -2.101383 1.203789
(0.32567) (0.31491)
[-6.45239] [ 3.82264]
SET -0.420384 -0.438546
(0.09796) (0.09472)
[-4.29149] [-4.62993]
STI 1.353018 -2.018991
(0.37949) (0.36695)
[ 3.56532] [-5.50208]
C 1.355101 2.913097
Note: cointegration with unrestricted intercepts and no trends.
Included observations: 1948 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
Table 3. Speed of Adjustment Parameter of the
Error Correction Term (ECT)
Error
Correction:
JAKCOMP
KLSE PSE SET STI
ect1 (α1) -0.004776 5.52E-05 0.009661 0.001528 0.004420
(0.00244) (0.00164) (0.00238) (0.00263) (0.00211)
[-1.95436] [ 0.03365] [ 4.05722] [ 0.58073] [ 2.09935]
ect2 (α2) -0.005994 -0.003282 -0.004991 0.001391 0.005767
(0.00303) (0.00203) (0.00295) (0.00326) (0.00261)
[-1.97914] [-1.61487] [-1.69136] [ 0.42634] [ 2.20980]
Note : cointegration with unrestricted intercepts and no trends
Included observations: 1948 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
As a common practice, Table 2 (APPENDIX)
shows that the first cointegrating vector is
normalized by JAKCOMP, while KLSE is
restricted to zero. Meanwhile, in the second one,
KLSE is used to normalize, while JAKCOMP is
restricted to zero. Based on t-statistic at the 5%
level of significance, JAKCOMP, PSE, SET, and
STI are found significant in the first cointegration
vector, while KLSE, PSE, SET, and STI are
significant in the second one. This means that all of
the significant indices (variables) significantly
contribute to the ASEAN indices’ long run
equilibrium.
With the same critical value of 5%, the speed of
adjustment coefficient for the first and second
cointegrating vector, for KLSE and SET are
statistically zero. This implies that both vectors
have no contribution to the convergence of these
indices to their long run paths, although SET does
have significant influence on any of the
cointegrating relationship, and KLSE affects only
the second one.
Atmadja: The Asean Stock Market Integration
7
In contrast, the speed of adjustment of
JAKCOMP, PSE, and STI are statistically
significant in both vectors. JAKCOMP has
negative influences in both cointegrating
relationship indicating a downward long run
adjustment. Conversely, STI affects the vectors
positively implying an upward long run
adjustment. In the second cointegrating vector,
JAKCOMP will react to a disequilibrium among
KLSE, PSE, SET, and STI. Thus, the vector would
contribute to the convergence of JAKCOMP to its
long run path, even though the index does not have
any significant contribution to the others return to
the long run equilibrium. PSE interestingly has
positive and negative significant impact on the first
and the second cointegration vectors, respectively.
The implication is that PSE would react positively
in the first vector, and negatively in the second one.
The existence of the cointegrating relationship
in the region during the time period could be
caused by some reasons. First, the degree of
economic integration in the ASEAN countries has
risen after the 1997 financial crisis. The
information barriers have also significantly decline
as a result of technological advance in IT
(information technology) and in the markets’
trading operating systems. The other reason is that
the degree of institutionalization and securitization
have increased in the regional market promoting
intra-regional fund transfers to increase
diversification opportunities.
After the VECM estimation is determined, the
next step is to search the existence of granger
causality among variables of each model. The
results of VEC Pairwise Granger Causality Tests
for each country are presented Table 4. Using a 5%
level of significance, the table shows only four
significant causality linkages found among the
variables in the pre crisis period. It also reveals
that none of the other ASEAN indices is
significantly granger caused JAKCOMP during the
period, vice versa. Thus, it may be concluded that
movements of the index during the period
apparently become isolated from the influence of
the others. STI experienced almost the same
condition as JAKCOMP when all other ASEAN
indices do not granger cause the index. However,
somewhat different with JAKCOMP, STI, as well
as SET, granger cause (in uni-directional form)
KLSE meaning that movements in KLSE
appeared to lag those of STI and SET. Moreover,
SET also appears to have bi-directional causality
with PSE.
As a part of the Accounting Innovation
Analysis, the impulse response analysis traces out
the responsiveness of the dependent variable in the
system to shocks to each of the variables (Brooks,
2002:341). The generalized type of the impulse
response analysis will be applied in this study to
observe short run dynamic interactions among the
variables, since orthogonalized impulse responses
is sensitive to the ordering of the variable in the
system. The complete result of the analysis is
presented in Table 5.
Table 4. VEC Pairwise Granger Causality/Block
Exogeneity Wald Tests
Dependent
variable Exclude Chi-sq Prob.
JAKCOMP KLSE 6.158533 0.4057
PSE 10.79299 0.0950
SET 9.533360 0.1457
STI 7.470766 0.2795
KLSE JAKCOMP 4.013962 0.6748
PSE 11.10882 0.0851
SET 12.83167 0.0458
STI 18.49538 0.0051
PSE JAKCOMP 12.17061 0.0583
KLSE 4.755074 0.5756
SET 40.46320 0.0000
STI 10.91111 0.0912
SET JAKCOMP 4.591306 0.5972
KLSE 10.01266 0.1241
PSE 13.22751 0.0396
STI 4.343344 0.6303
STI JAKCOMP 12.14867 0.0587
KLSE 9.910047 0.1285
PSE 8.841288 0.1827
SET 9.852119 0.1310
Table 5. The Impulse Response to Generalized One
S.D. Innovations
Response
of
Period JAKCOMP
KLSE PSE SET STI
JAKCOMP 1 0.012863 0.003140 0.002791 0.003167 0.004256
2 0.013992 0.003609 0.003377 0.003920 0.005196
3 0.013608 0.004138 0.003762 0.004475 0.005381
4 0.013862 0.004212 0.004669 0.005340 0.006202
5 0.014158 0.004193 0.005338 0.005551 0.006843
6 0.014500 0.004412 0.005403 0.006451 0.007608
7 0.014227 0.004330 0.005254 0.006773 0.007709
KLSE 1 0.002107 0.008631 0.001743 0.002438 0.003392
2 0.002839 0.010238 0.001909 0.003186 0.004508
3 0.002793 0.010509 0.001585 0.003610 0.004519
4 0.003137 0.010794 0.001569 0.003572 0.005168
5 0.003293 0.010699 0.001611 0.003504 0.005511
6 0.003476 0.010717 0.001286 0.003923 0.005946
7 0.003373 0.010630 0.001105 0.003980 0.005859
PSE 1 0.002720 0.002532 0.012534 0.002354 0.002792
2 0.004037 0.003496 0.013744 0.004353 0.004535
3 0.003873 0.003554 0.013203 0.004698 0.004649
4 0.004664 0.003473 0.012587 0.005219 0.005467
5 0.004522 0.003449 0.012576 0.005347 0.005576
6 0.004933 0.003293 0.011914 0.006350 0.005723
7 0.005139 0.003108 0.011751 0.006768 0.005631
SET 1 0.003410 0.003914 0.002602 0.013853 0.005060
2 0.003756 0.004555 0.003256 0.013892 0.005480
3 0.003845 0.004985 0.003782 0.014790 0.006311
4 0.004091 0.005459 0.004515 0.014805 0.006699
5 0.003881 0.004818 0.004692 0.014721 0.006660
6 0.003922 0.005431 0.004932 0.015535 0.007066
7 0.003364 0.004790 0.004150 0.014909 0.007037
STI 1 0.003667 0.004355 0.002469 0.004048 0.011083
2 0.003284 0.003814 0.002652 0.004329 0.011242
3 0.002991 0.003888 0.002978 0.004751 0.011037
4 0.003094 0.003998 0.002944 0.004987 0.011528
5 0.002847 0.004416 0.003423 0.005319 0.012042
6 0.002970 0.004458 0.003290 0.005579 0.012248
7 0.002747 0.004464 0.003485 0.005614 0.011606
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
8
As can be seen in Table 5, a generalised
impulse response analysis indicates that one
standard error shock to JAKCOMP would result in
a positive response by changes in STI of 0.0037,
one step ahead. Afterward, the responses have
become smaller ever since. A shock to STI,
commonly believed as the most prominent stock
index in ASEAN, results in second greatest
changes in the other indices in the short run
period. Meanwhile, the greatest contemporaneous
reaction of an index generally due to its own
shocks. This indicates that internal/domestic
shocks in a particular index may have greatest
significant impacts on its movements, and STI
become the most influential stock index in the
region at the time period.
While impulse response functions trace the
effects of a shock to one endogenous variable on to
the other variables in the system, variance
decomposition separates the variation in an
endogenous variable into the component shocks to
the system. As it is mentioned by Enders
(2004:280) the forecast error variance
decomposition tells the proportion of the
movements in a sequence due to its own shock
versus shock to the other variable. A shock to the ith
variable will not only affect that variable, but
can also be transmitted to all of the other variables
in the system.
Table 6 presents the result of the forecast error
variance decomposition of the serries in the period
of before financial crisis. As can be seen from the
table, in general, the proportion movements of the
indices are dominantly due to their own shocks.
Surprisingly, only around 70% of the error variance
of STI was attributable to own shocks in the steps
ahead, while JAKCOMP contributed maximum of
11% to STI’s error variance.
The Period of the 2007 Financial Crisis
The ADF test conducted to the serries at level
reveals the presence of unit root in the serries. The
lags order test then shows three lags length as the
appropriate lag order since the residual is not
serially correlated. However, the Johansen
Cointegration test fails to find the existence of
cointegration vector in the serries. This concludes
that the serries has no cointegrating relationship.
In other words, the indices have no long run
equilibrium during the 2007 financial crisis. The
finding somewhat contradicts with the ones given
by some other researchers (Arshanapalli et al 1993;
Sheng et al 2000, and Yang et al 2003), but
confirms that of Roca (2000:145).
The absence of cointegrating vector in the
series indicates that the cointegration analysis
framework is not possible to be carried out. Hence,
the VAR analysis framework would be applied to
estimate the relationship of the indices, as well as
to reveal the short run dynamic interactions among
the indices.
Table 6. Variance Decomposition
Variance Decomposition of JAKCOMP:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.012863 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.019020 99.85659 0.010939 0.028547 0.049753 0.054174
3 0.023434 99.50386 0.136144 0.109426 0.186402 0.064169
4 0.027354 98.70463 0.197381 0.416767 0.512016 0.169204
5 0.030979 97.84212 0.214017 0.842533 0.727739 0.373589
6 0.034447 96.85652 0.241250 1.088327 1.165416 0.648487
7 0.037525 95.98986 0.258696 1.232197 1.641083 0.878166
Variance Decomposition of KLSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.008631 5.960468 94.03953 0.000000 0.000000 0.000000
2 0.013407 6.954068 92.87426 0.028773 0.040597 0.102303
3 0.017064 6.971806 92.59963 0.141095 0.195879 0.091595
4 0.020238 7.359674 91.94159 0.229436 0.209507 0.259789
5 0.022955 7.778958 91.22487 0.270537 0.201897 0.523735
6 0.025444 8.198359 90.24934 0.397181 0.289759 0.865363
7 0.027681 8.411056 89.59566 0.531422 0.381012 1.080849
Variance Decomposition of PSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.012534 4.709320 2.360945 92.92974 0.000000 0.000000
2 0.018719 6.761489 2.970640 89.41751 0.670515 0.179843
3 0.023044 7.286824 3.322941 87.93643 1.180083 0.273723
4 0.026505 8.605063 3.336361 85.70276 1.768701 0.587110
5 0.029573 9.250590 3.348796 84.34696 2.224120 0.829537
6 0.032241 10.12412 3.263569 82.40609 3.231500 0.974721
7 0.034710 10.92704 3.118762 80.65164 4.273855 1.028701
Variance Decomposition of SET:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.013853 6.060589 5.260581 1.058338 87.62049 0.000000
2 0.019639 6.673187 6.267257 1.496936 85.55756 0.005057
3 0.024625 6.681866 6.857899 1.896486 84.48702 0.076732
4 0.028819 6.893286 7.554222 2.489728 82.92112 0.141642
5 0.032441 6.871721 7.475509 3.036180 82.40051 0.216083
6 0.036054 6.746606 7.689571 3.399525 81.89916 0.265139
7 0.039080 6.483311 7.641728 3.442428 82.03551 0.397027
Variance Decomposition of STI:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.011083 10.94870 10.36302 1.108366 4.525942 73.05397
2 0.015812 9.692145 8.950799 1.464531 5.434735 74.45779
3 0.019334 8.876028 8.824737 1.942618 6.518288 73.83833
4 0.022557 8.401438 8.680124 2.081363 7.188879 73.64820
5 0.025643 7.733370 8.956077 2.414139 7.690111 73.20630
6 0.028485 7.354068 9.084298 2.515628 8.205909 72.84010
7 0.030842 7.066422 9.358043 2.738126 8.729031 72.10838
The VAR analysis, however, requires that the
series must be stationary. Hence, the non
stationary series may be made stationary by
differencing or detrending process. After
transforming the serries into first difference form,
the ADF test is re-employed to ensure that the
series are now stationary. The lag order test then
indicated that the appropriate lag length would be
three. After estimating the series using the VAR in
first difference analysis, the estimated models can
be shown in Table 7.
Table 8 shows the results of a block causality
test implemented on the series. The table reveals
that, using a 5 % level of significance, more
variables significantly granger cause another in the
crisis period compared to those in pre crisis period.
It means that there are more variables that their
current values have correlation with the past (lags)
value of another implying that the present value of
an index can be predicted with greater accuracy by
using past value of another. This then indicates
that there is an increase in causal linkages among
Atmadja: The Asean Stock Market Integration
9
those indices in the region during the crisis period.
The results are in fact different with those before
the crisis period showing a changing behaviour in
the indices’ movements. For instance, the lags of
SET and STI now significantly enter into the
equation for JAKCOMP, while in the pre crisis
period does not.
Table 7. Vector Autoregression Estimates
JAKCOMP KLSE PSE SET STI
JAKCOMP(-1) 0.072296 0.134318 0.132358 0.101597 0.029438
(0.06530) (0.03643) (0.05607) (0.05610) (0.06375)
[ 1.10716] [ 3.68735] [ 2.36041] [ 1.81101] [ 0.46179]
JAKCOMP(-2) 0.120791 0.083802 0.143389 0.198738 0.096179
(0.06664) (0.03718) (0.05723) (0.05726) (0.06506)
[ 1.81247] [ 2.25413] [ 2.50552] [ 3.47108] [ 1.47829]
JAKCOMP(-3) -0.058141 -0.010836 -0.017686 -0.026591 0.005682
(0.06641) (0.03705) (0.05703) (0.05706) (0.06484)
[-0.87543] [-0.29248] [-0.31011] [-0.46604] [ 0.08764]
KLSE(-1) -0.208902 -0.185895 -0.070152 -0.186469 -0.231196
(0.10815) (0.06033) (0.09287) (0.09291) (0.10558)
[-1.93162] [-3.08130] [-0.75537] [-2.00693] [-2.18980]
KLSE(-2) -0.210401 -0.180766 -0.102958 -0.375966 -0.275983
(0.10879) (0.06069) (0.09342) (0.09346) (0.10620)
[-1.93407] [-2.97869] [-1.10212] [-4.02270] [-2.59867]
KLSE(-3) 0.062985 0.124513 0.038155 0.027387 0.117089
(0.10840) (0.06047) (0.09308) (0.09313) (0.10582)
[ 0.58106] [ 2.05914] [ 0.40990] [ 0.29409] [ 1.10649]
PSE(-1) 0.017456 0.029010 -0.043004 0.048177 0.012092
(0.06255) (0.03489) (0.05371) (0.05374) (0.06106)
[ 0.27907] [ 0.83140] [-0.80062] [ 0.89653] [ 0.19802]
PSE(-2) 0.051068 0.048802 0.014394 0.077747 0.058784
(0.06224) (0.03472) (0.05345) (0.05347) (0.06076)
[ 0.82050] [ 1.40558] [ 0.26931] [ 1.45401] [ 0.96747]
PSE(-3) -0.020801 -0.019708 -0.040438 0.010962 -0.049437
(0.06019) (0.03358) (0.05168) (0.05171) (0.05876)
[-0.34561] [-0.58698] [-0.78239] [ 0.21200] [-0.84138]
SET(-1) 0.135694 -0.014998 0.029879 -0.069210 -0.078839
(0.07317) (0.04082) (0.06283) (0.06286) (0.07143)
[ 1.85453] [-0.36744] [ 0.47554] [-1.10100] [-1.10371]
SET(-2) -0.043142 0.019411 -0.125749 0.016529 0.040208
(0.07254) (0.04047) (0.06229) (0.06232) (0.07082)
[-0.59473] [ 0.47969] [-2.01868] [ 0.26523] [ 0.56778]
SET(-3) -0.149188 -0.042758 -0.077355 -0.060061 -0.147843
(0.07241) (0.04039) (0.06218) (0.06221) (0.07069)
[-2.06042] [-1.05858] [-1.24410] [-0.96552] [-2.09154]
STI(-1) 0.151469 0.077991 0.230158 0.060714 0.124110
(0.07280) (0.04061) (0.06252) (0.06255) (0.07107)
[ 2.08051] [ 1.92034] [ 3.68142] [ 0.97068] [ 1.74621]
STI(-2) 0.048648 0.026292 0.062191 0.044742 0.028764
(0.07342) (0.04096) (0.06305) (0.06308) (0.07168)
[ 0.66258] [ 0.64191] [ 0.98637] [ 0.70931] [ 0.40130]
STI(-3) 0.155315 -0.012852 0.046721 0.128301 0.021717
(0.07118) (0.03971) (0.06113) (0.06116) (0.06949)
[ 2.18190] [-0.32366] [ 0.76432] [ 2.09796] [ 0.31251]
C -0.000179 -0.000638 -0.000853 -0.000801 -0.001001
(0.00093) (0.00052) (0.00080) (0.00080) (0.00091)
[-0.19335] [-1.23409] [-1.07116] [-1.00633] [-1.10644]
Note: Standard errors in ( ) & t-statistics in [ ]
5 % level of significant
Table 8. VAR Pairwise Granger Causality/Block
Exogeneity Wald Tests
Dependent
variable Exclude Chi-sq Prob.
JAKCOMP KLSE 7.633367 0.0542
PSE 0.892896 0.8271
SET 7.966430 0.0467
STI 9.699188 0.0213
KLSE JAKCOMP 18.50092 0.0003
PSE 3.030814 0.3869
SET 1.542379 0.6725
STI 4.229433 0.2377
PSE JAKCOMP 11.80429 0.0081
KLSE 1.972551 0.5781
SET 5.832183 0.1201
STI 15.30169 0.0016
SET JAKCOMP 15.51245 0.0014
KLSE 19.47387 0.0002
PSE 2.766382 0.4291
STI 5.971454 0.1130
STI JAKCOMP 2.368227 0.4996
KLSE 12.88606 0.0049
PSE 1.790673 0.6170
SET 6.127043 0.1056
In order to capture the short run dynamic
interaction among the variables during the
financial crisis period, the generalized impulse
response and the forecast error variance
decomposition, would also be employed. The results
of the generalized impulse response analysis of the
series are presented in Table 9. As it is shown in
the table, during the financial crisis, the
generalised impulse response analysis indicates
that all variables gave greater immediate reactions
to a shock of one variable compared to those in the
pre-crisis era. This implies that the short run
interaction between two indices became more
intense during the 2007 financial crisis period. In
other words, the findings strongly indicate that the
ASEAN indices become more interdependent
during the financial crisis, although they had no
long run equilibrium.
The variance decomposition analysis (Tabel
10) reveals that the proportion of the movements in
an index due to its own shock for all indices
declined during the financial crisis. This means
that in the period of the financial crisis shocks to
other indices have more explanatory power to the
movements of a particular index in the s-steps
ahead. This finding seems reinforce the result of
generalized impulse response analysis that during
the 2007 financial crisis period, the ASEAN’s stock
indices tend to be more interdependent. Thus, it
somewhat confirmed the previous researches done
by Roca (2000:145) and Yang et al (2003:478)
which conclude that interdependency and causal
linkages among the indices become more
significant during crisis.
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
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Table 9. The Impulse Response to Generalized
One S.D. Innovations
Response
of
Period JAKCOMP KLSE PSE SET STI
JAKCOMP 1 0.020461 0.011579 0.008953 0.011621 0.013632
2 0.003635 0.001526 0.002198 0.004214 0.004293
3 0.001716 -0.000584 0.000966 0.000523 0.001221
4 0.000100 0.000434 -0.000181 -0.000541 0.001408
5 -0.000507 -0.000267 -0.000492 -0.000799 -0.000178
6 -0.000571 -0.000327 -0.000373 -0.000433 -0.000329
7 -0.000532 -0.000227 -0.000441 -0.000583 -0.000670
KLSE 1 0.006459 0.011414 0.005095 0.005452 0.006781
2 0.002659 0.000461 0.001297 0.001534 0.002167
3 0.001598 -0.000120 0.001140 0.001414 0.001395
4 -4.01E-05 0.000663 -0.000214 -0.000399 5.15E-05
5 8.40E-05 -0.000142 -0.000101 -0.000276 0.000126
6 -0.000133 -7.84E-05 -8.03E-05 -7.53E-05 8.39E-05
7 -0.000223 -3.72E-05 -0.000149 -0.000202 -0.000250
PSE 1 0.007689 0.007843 0.017570 0.007738 0.007307
2 0.005286 0.003377 0.002215 0.004376 0.005958
3 0.002075 -2.87E-05 0.000618 -4.34E-05 0.001447
4 -0.000102 -0.000320 -0.000544 -0.000358 0.000359
5 -0.000711 -0.000156 -0.000596 -0.000995 -0.000434
6 -0.000525 -0.000297 -0.000408 -0.000535 -0.000410
7 -0.000345 -0.000107 -0.000214 -0.000261 -0.000347
SET 1 0.009984 0.008397 0.007742 0.017578 0.011578
2 0.001362 -0.000435 0.000775 0.000119 0.000884
3 0.003034 -0.000533 0.001747 0.002050 0.002101
4 0.000804 0.001367 0.000655 0.000834 0.002134
5 -0.000160 -0.000455 -0.000346 -0.000645 -0.000140
6 -0.000169 -0.000252 -0.000108 -0.000116 0.000174
7 -0.000474 -0.000120 -0.000405 -0.000544 -0.000503
STI 1 0.013308 0.011867 0.008307 0.013156 0.019974
2 6.65E-05 -0.001392 -0.000281 -0.000578 0.000488
3 0.000879 -0.001056 0.000734 0.000894 0.000598
4 -0.001135 0.000113 -0.001453 -0.002127 -0.000913
5 -0.000329 -0.000385 -0.000354 -0.000535 -0.000465
6 -0.000392 -5.06E-05 -0.000155 -9.08E-05 -8.33E-05
7 -0.000300 -0.000110 -0.000214 -0.000306 -0.000493
Table 10. Variance Decomposition
Variance Decomposition of JAKCOMP:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.020461 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.021073 97.24338 0.093183 0.174688 1.656038 0.832708
3 0.021264 96.15798 0.878554 0.303776 1.644745 1.014946
4 0.021412 94.83229 0.912108 0.332163 1.758313 2.165129
5 0.021436 94.68301 0.910264 0.353508 1.830443 2.222772
6 0.021445 94.67600 0.909518 0.357529 1.831413 2.225544
7 0.021458 94.61447 0.910093 0.372423 1.851690 2.251322
Variance Decomposition of KLSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.011414 32.02605 67.97395 0.000000 0.000000 0.000000
2 0.011842 34.79170 64.28911 0.182911 0.037178 0.699104
3 0.012084 35.16451 62.80328 0.676161 0.434642 0.921404
4 0.012135 34.86724 62.73966 0.813231 0.661356 0.918514
5 0.012148 34.79750 62.64135 0.817565 0.737312 1.006279
6 0.012152 34.78759 62.60206 0.817460 0.736878 1.056009
7 0.012156 34.79723 62.56663 0.822289 0.743584 1.070267
Variance Decomposition of PSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.017570 19.14909 5.811252 75.03966 0.000000 0.000000
2 0.018664 24.99113 5.212981 66.52072 0.824013 2.451155
3 0.018915 25.53371 5.670186 64.76357 1.220127 2.812400
4 0.018949 25.44521 5.678093 64.59816 1.227054 3.051487
5 0.018986 25.48622 5.680653 64.39330 1.373668 3.066158
6 0.018997 25.53520 5.674576 64.33616 1.390703 3.063358
7 0.019001 25.55543 5.674949 64.30681 1.392098 3.070709
Variance Decomposition of SET:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.017578 32.25784 3.592081 2.848586 61.30150 0.000000
2 0.017729 32.29985 4.211044 2.919796 60.38029 0.189017
3 0.018269 33.17686 6.197357 3.213120 57.06075 0.351916
4 0.018426 32.80436 6.452377 3.159159 56.10525 1.478857
5 0.018446 32.74148 6.496111 3.163665 56.07662 1.522124
6 0.018455 32.71765 6.500189 3.160603 56.02145 1.600115
7 0.018468 32.73729 6.500537 3.178835 55.97476 1.608586
Variance Decomposition of DLNSTI:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.019974 44.38852 6.931322 0.493399 7.788191 40.39857
2 0.020116 43.76814 7.577686 0.490308 7.717361 40.44651
3 0.020262 43.32822 8.333029 0.692744 7.747659 39.89834
4 0.020443 42.86986 8.386302 1.122186 8.404362 39.21729
5 0.020451 42.86178 8.393528 1.128666 8.426711 39.18932
6 0.020457 42.87448 8.399131 1.128383 8.425800 39.17221
7 0.020464 42.86514 8.394334 1.130788 8.426286 39.18345
CONCLUSION
The study concludes that two cointegrating
vectors are found in the series before the 2007
financial crisis period indicating the existing of long
run equilibrium in the series during the time
period. However, the study fails to find any
cointegrating vector in the series during the
financial crisis period. The results prove that the
long run relationship of the ASEAN indices has
been removed by the 2007 financial crisis.
The block causality tests employed in both subsample
period reveal that more significant causal
linkages are found in the series during the
financial crisis period compared to those before the
financial crisis. The accounting innovation
analyses conducted to the series also indicate that
the short run dynamic interactions among the
indices tend to be more intense during the financial
crisis period. These all indicate that the indices
become more interdependent during the financial
crisis period since the moment gives rise the
explanatory power of a sequence to the movements
of another.
The general conclusion that may be withdrawn
from this study is that the contagious effect of the
2007-US financial crisis has affected the ASEAN’s
capital market integration, and has changed the
behaviour of the indices’ movements both in the
short run and in the long run.
Thus, the implication policy that can be
suggested is that the diversification of portfolio
within the ASEAN stock markets in the short run
is unlikely to reduce the risk due to the high degree
of financial interdependent of these markets
during the financial crisis.
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Jurnal Akuntansi-B.Inggris

Mei 12, 2010

The Asean Stock Market Integration:
The Effect of the 2007 Financial Crisis on the Asean Stock Indices’
Movements
Adwin Surja Atmadja
Faculty of Economics, Petra Christian University
E-mail: aplin@peter.petra.ac.id
ABSTRACT
This study attempts to examine the existence of cointegration relationship and the short
run dynamic interaction among the five ASEAN stock market indices in the period of before
and during the 2007 financial crisis. The multivariate time series analysis frameworks are
employed to the series in both sub-sample periods in order to answer the hypotheses.The
study finds two cointegrating vectors in the series before the financial crisis period, however
it fails to detect any cointegrating vector in the period of financial crisis. Granger causality
tests applied to the series reveal that number of significant causal linkages between two
variables increase during the crisis period. Moreover, the accounting innovation analysis
shows an increase in the explanatory power of an endogenous variable to another within the
system during the crisis period, indicating that the contagious effect of the 2007-US financial
crisis has entered into the ASEAN capital market, and significantly influenced the regional
indices’ movements.
Keywords: ASEAN, stock market integration, the 2007 financial crisis, regional indices’
movements.
INTRODUCTION
Liberalization of the five ASEAN (Indonesia,
Malaysia, the Philippines, Singapore, and
Thailand) financial markets in 1980s resulted in
enormous capital inflows to this region. By opening
their national borders for foreign investors, the
countries’ financial markets were overwhelmed by
foreign capital in both foreign direct and portfolio
investments giving significant support to their
rapid domestic economic development, as well as
enjoyed rapid financial markets expansion in the
beginning of 1990s. Capital inflows have been
crucial to the rapid – sustained growth in ASEAN
countries (Sachs and Larrain, 1993:577) at that
time, since domestic saving, as commonly in
developing countries, had little role as development
funding.
Triggered by the sharp depreciation of the
Thai baht in the midst of 1997, the disastrous
effects of the 1997 financial crisis were broadly
spread out to the countries’ financial markets
which were dominated by bank loan and portfolio
investment, not by foreign direct investment
(DFAT, 1999:29). The crisis then extensively
affected the world financial markets through its
contagion effects. Market capitalization of the
countries’ stock market was largely contracted due
to a deep depreciation in their stock prices causing
their stock indices then sharply plunged.
However, the downturn in the five ASEAN
rebounded in 1999. After the sharp output
contraction in 1998, growth returned in that year
as depreciated currencies spurred higher exports
(Krugman and Obstfeld, 2003:693). Following the
appreciation of regional currencies in the second
semester of the year, the regional capital and
financial markets started to recover. The regional
stock market indices increased around 42.46% on
average compared to those from two years before
(calculated from IFS 2004). This might indicate
that investors’ confidence started to recover and
they began to invest in the five ASEAN.
During ten years after, the ASEAN’s
economies steadily grew to their new equilibrium.
As a market indicator, the ASEAN capital market
indices apparently fluctuated in a relatively narrow
range dominantly due to small internal shocks in
the short run, but stably moved with positive
trends in the long run. These all mirror that the
ASEAN markets were relatively stable during the
time periods, and their economies were just on the
right tracks.
However, in the second semester of 2007 the
countries experienced significant shocks in their
capital markets due to a contagious effect of the US
financial market turmoil. At the time, the US
financial market deeply suffered from the most
1
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
2
significant economic shocks initiated by the subprime
mortgage crisis leading to the downturn in
housing market, and then worsened by the spike in
commodity prices (Yellen 2008:1). The devastating
effects of the 2007 financial crisis in the US then
widely spread throughout the world.
From the facts above, the 2007 financial crisis
may have significant consequences on the variation
of the countries’ stock indices that probably
different with those in non crisis era. The financial
crisis could possibly cause the regional indices
deviate from their long run equilibrium, and the
behaviour of the indices’ movements may be
different with those before. All possibilities may
happen in the regional market depended on how
significant the impact of the financial crisis hit the
market. Therefore, this study will empirically
examine how the 2007 financial crisis has taken
into effect on the five ASEAN stock indices’
movements. To be more specific, this study
attempts to observe the existing of cointegrating
relationships among the five ASEAN stock indices
in the periods of before (pre) and during the 2007
financial crisis in order to portray the long run
interrelations among the indices in the both
periods. The aim is also to answer how and to what
extent the stock indices dynamically interact with
each other in the short run during the given
periods.
CONCEPT OF FINANCIAL MARKET OR
STOCK MARKET INTEGRATION
The basic theoretical concept of financial
market or stock market integration is adopted from
the law of one price. In integrated financial
markets, the assets with the same risk in different
markets will result in the same yield when
measured in a common currency (Stulz 1981:924-
5). However, if the yields are different across the
markets, the arbitrage process will play an
important role in eliminating the differences.
Operationally capital markets integration refers to
the extent that markets’ participants are enabled
and obligated to take notice of events occurring in
other markets by using all available information
and opportunities, while financial market
integration is defined in terms of price
interdependence between markets (Kenen 1976:9).
Moreover, stock market integration is affected by
some factors (Roca 2000:14), such as:
1. Economic integration, which means that the
more integrated the economies of countries, the
more integrated their equity markets (Eun and
Shim 1989: 256).
2. Multiple listing of stocks. This implies that a
shock in a particular stock market can be
transmitted to other stock market through
shares listed in both markets.
3. Regulatory and information barriers. The
higher the barriers, the lower the degree of
stock market integration.
4. Institutionalisation and securitisation. As
institutions are more willing to transfer funds
overseas to increase their diversification
opportunities, the integration will be promoted.
5. Market contagion. The prices between stock
markets can move together due to a contagion
effect (King and Wadwhani 1990:5), and this
contagion effect determines significantly the
dynamic relationships between international
stock markets (Climent and Meneu, 2003:111).
However, in emerging stock markets, this effect
might be smaller than what is widely perceived
(Pretorius 2002:103).
Much research has been done, mainly by using
a cointegration analytical framework, to find and
analyse the existence of integration in stock
market across countries. The results are different
depending on where, when, and how the research
has being conducted. The cointegration analytical
framework has been widely applied to examine the
integration of stock markets across countries. Once
a cointegration vector is found among two or more
stock markets, it indicates the existence of a long
run relationship among them. Thus, stock price
movements in one equity market will affect
another in other markets.
A research conducted by Chung and Liu
(1994:55) found two cointegration vectors between
the U.S and larger Asia Pacific stock markets.
Palac-McMiken (1997:299) also reveals the
existence of cointegration in ASEAN markets
(Malaysia, Singapore, Thailand, and the
Philippines), except Indonesia, during 1987 to
1995. Both results were confirmed by Masih and
Masih (1999:275) who report that some of ASEAN
countries (Thailand, Malaysia, and Singapore)
have a high degree of interdependence with other
Asian (Hong Kong and Japan) and developed (the
U.S. and the U.K.) stock markets. Furthermore,
they also find one cointegration vector among
several major Asian stock markets (Hong Kong,
Korea, Singapore, and Taiwan) and major
developed markets (Masih and Masih 2001: 580-1).
Interestingly, Pretorius (2002:103) reports that
the degree of bilateral trade and the industrial
production growth differential significantly
explained the correlation between two equity
markets, and that the stock markets of countries in
the same region are more interdependent than
those in different regions. Consistent with this
finding, Roca (2000:145) finds the existence of
Atmadja: The Asean Stock Market Integration
3
interdependency among all the ASEAN stock
markets in the short run. However, in contrast to
short run interdependency, he indicates that there
was no cointegration among ASEAN countries as a
group during 1988-1995 and that those stock
markets were not significantly related to each
other in the long run.
Chan, Gup and Pan (1992:289) and DeFusco,
Geppert and Tsetsekos (1996:343) also mention
that there is no cointegration between the U.S and
several Asian emerging stock markets (Hong Kong,
Taiwan, Singapore, Korea, Malaysia, Thailand,
and the Philippines) in the 1980s and early 1990s.
However, these findings somewhat contradicts
with those of Chung et al. (1994) and Masih et al.
(1999). This then implies that the interdependence
among stock markets is not stable over time. For
example, Hung and Cheung (1995:286) assert that
there is no cointegration among stock markets in
some Asia-Pacific countries (Malaysia, Hong Kong,
Korea, Singapore, and Taiwan). However, when
they used US dollar denominated stock prices, it
was reported that those stock markets were
cointegrated after, but not before, the 1987 stock
crash.
Arshanapalli and Doukas (1993:206) also
mention the instability of stock market
interdependence when they tested the effect of
inclusion or omission of the data for the 1987 crisis
and revealed that that it affects the results. They
conclude that the stock markets were highly
integrated during the crisis. Furthermore,
Arshanapalli, Doukas and Lang (1995:72) show
that after the 1987 crisis the stock markets in
emerging markets (Malaysia, the Philippines, and
Thailand) and developed markets (Hong Kong,
Singapore, the U.S., and Japan) are more
interdependent as they found cointegration in the
post-crisis period, but not in the pre-crisis period.
Other researchers, Liu, Pan and Shieh (1998: 59)
also confirm that there is an increase in the
interdependence within Asian-Pacific regional
markets and the stock markets in general post-the
1987 crisis. Similarly, Sheng and Tu (2000:245)
document one cointegration vector between the
U.S. and several Asian stock markets (Taiwan,
Malaysia, China, Thailand, Indonesia, South
Korea, the Philippines, Australia, Japan, Hong
Kong, and Singapore) during the crisis, but none in
the year before the crisis, when they observed the
stock markets using daily data.
Finally, a research recently conducted by
Yang, Kolari and Min (2003:478) examined the
long-run relationship and short-run dynamic
causal linkages among the U.S, Japanese, and ten
Asian emerging markets using daily data of 1997-
1998 periods. They confirm that the stock markets
of those countries have been more integrated after
the 1997 Asian financial crisis than before the
crisis. Both long-run cointegration relationship and
short-run causal linkages among those markets
become more significant during the crisis. These
findings also confirm that the degree of integration
among those countries tends to change over time.
Several points that may be drawn form the
literature review. The implication is that
liberalization of the financial sector in many
countries has caused world or regional stock
markets to be more integrated. Empirical evidence
is given by the presence of cointegration vectors
and significant short-run causal linkages. It is
worth noting that the stock markets of countries in
the same region may be more interdependent than
those in different regions.
RESEARCH METHODOLOGY
Basically, a stock market price index or stock
market index is a portfolio of individual stocks. The
index level corresponds to some average of the
price levels of individual shares. Changes in the
index level give rise to market returns. Thus, the
stock market index, which can be viewed simply as
a portfolio of shares, can commonly be use as an
indicator of the market performance. There are
several factors that determine the level of the
index, such as breadth of index, weighting system,
capitalization adjustment, and dividend effect
(Brailsford Heaney and Bilson 2004:68).
The stock market index of a country may also
be an indicator of short-term portfolio investment
movement in the country. An upward trend of a
stock market index means that there is an increase
in demand of the listed shares in the market. This
indicated that investors are attracted to buy shares
and invest their fund in the country. On the other
hand, a downward trend movement of a stock
market index indicates that the investors are
unlikely to continuously hold the listed shares.
Hence, stock market movements may reflect the
attractiveness of a country for investments,
especially for portfolio investments.
In this study, the daily closing stock price
indices of the five ASEAN countries, which are
Jakcomp of Indonesia; KLSE of Malaysia; PSEi of
the Philippines; STI of Singapore; and SET
Composite of Thailand, are employed as
measurement of the countries’ daily stock index
movements in the periods of before and during the
2007 financial crisis.
Some previous research (Arshanapalli et al,
1993, Chung et al, 1994, Arshanapalli et al, 1995,
Liu et al,1998, Masih et al., 1999, Masih et al,
2001) document that stock markets in the Asian
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4
region are interdependent not only among
themselves, but also with some of the developed
market. Furthermore, those stock markets are
even more interdependent during and after the
financial crisis (Sheng et al 2000; Yang et al 2003)
In the case of the ASEAN, Palac-McMiken
(1997:299) reports the existence of cointegration in
the countries’ stock markets, except Indonesia,
before the 1997 crisis. Yang et al (2003:478)
confirm that both long-run cointegration
relationship and short-run causal linkages among
those markets become more significant during the
crisis period. In contrast, Roca (2000:145) finds the
existence of interdependency among the five
ASEAN’s stock markets in the short run, but not
significantly related in the long run before the 1997
crisis.
Based on these findings, it is hypothesized that
the ASEAN stock indices would have long run
cointegration relationship and short run dynamic
interaction, and that the relationship and the
interaction would be more significant during the
2007 financial crisis.
All daily price index data of the five ASEAN
during the observation periods are obtained from
the Thomson Financial. The index data of all
variables then will be transformed into natural
logarithm forms before conducting the analyses.
In order to examine the movements of the
indices in both periods, the data are then separated
into two sub-sample periods, which are the periods
of: 1) Before the 2007 financial crisis (pre crisis),
which cover the period of Jan 2000 – June 2007, 2)
During the 2007 financial crisis, which cover the
period of July 2007 – May 2009, as it is stated in
several publications (http://en.wikipedia.org,www.
globalissues.org,www.atypon-link.com)
The two most appropriate models that one of
which may suitable for this study are VAR and
VECM. In the Vector autoregressive model (VAR)
all of the variables are endogenous, and
symmetrically treated. A VAR could be very large,
however the simplest VAR model, in standard
form, could be written as (Enders, 2004:265):
Yt = a10 + a11Yt-1 + a12 Zt-1 + eYt.
Zt = a20 + a21Yt-1 + a22 Zt-1 + εZt.
The VAR requires that all variables be
stationary and the appropriate lag length is data
driven (Brooks 2002:333). There are several
available tests for testing for a unit root, the most
common is the Augmented Dicky-Fuller (ADF)
test. Non-stationary variables may be made
stationary by differencing or detrending process.
To define the appropriate lag length, some
tests of information criteria that will be applied in
this study include the likelihood ratio test; Akaike
Information Criterion (AIC); and Schwarz
Bayesian Criterion (SC).
The likelihood ratio test is based on asymptotic
theory and is an F-type approximation. This test
actually compares a restricted VAR (less lags) to an
unrestricted VAR (more lags). Thus, the null
hypothesis of this test is that the restricted model
is correct. However, the shortcoming of this test is
that it may not be useful in small samples. In
addition, the likelihood ratio test is only valid when
the restricted model is tested (Enders 2004:283).
Because of the limitations of the likelihood
ratio test, multivariate generalization of AIC and
SC may be the most suitable alternatives. The
minimum values of AIC and/or SC may validly
indicate the appropriate lags length, as long as the
model’s residual has no serial correlation problem.
Otherwise, the lag length may be too short. Thus, it
is necessary to re-estimate the model using lag
length that yield serially uncorrelated (Enders
2004:338).
In VAR, a block causality test will be used to
examine whether the lags of one variable enter into
the equation for another variable (Enders
2004:283). A variable (y1) is said to be a grangercause
of another (y2) if the present value of y2 can
be predicted with greater accuracy by using past
values of y1, all other information being identical
(Thomas 1997:461). If y1 granger-causes y2, then
the parameters of lags of y1, βi’s, should not equal
zero in the equation of y2. However, it is worth
noting that granger-causality basically means a
correlation between the current value of one
variable and the past (lags) value of others. It does
not mean that movements of one variable
physically cause movements of another (Brooks,
2002:240). Granger causality simply implies a
chronological ordering of movements of the series.
Therefore, it could validly be stated that changes or
movements in one variable (y2) appear to lag those
of another (y1).
The alternative model that probably suitable
to be used is the vector error correction model
(VECM) or cointegration framework analysis,
which is basically is a VAR augmented by the error
correction term (êt-1). The simplest VECM, in
general, takes the form as (Enders 2004:329):
ΔYt = α10 + αY êt-1+ Σ α11(i) ΔYt-i + Σ α12(i) ΔZt-i + εYt.
ΔZt = α20 + αZ êt-1+ Σ α21(i) ΔYt-i + Σ α22(i) ΔZt-i + εYt.
where
êt-1 = (Yt-1 – β1Z1t-1)
Thus, if the parameters of error correction
term (ECT), called speed of adjustments (αY and αZ)
in VECM, are zero, then VECM reverts to a VAR
in first differences (Enders 2004:329).
ΔYt = α10 + Σ α11(i) ΔYt-i + Σ α12(i) ΔZt-i + εYt.
ΔZt = α20 + Σ α21(i) ΔYt-i + Σ α22(i) ΔZt-i + εYt.
However, if the speed of adjustments are not
zero, the larger the speed of adjustments, the
Atmadja: The Asean Stock Market Integration
5
greater the response to previous periods’ deviation
from the long run equilibrium. Thus, a
cointegration relationship is a long term or
equilibrium phenomenon, since it is possible that
cointegrating variables may deviate from their
relationship in the short run, but their association
would return in the long run. A principal feature of
cointegrated variable is that their time paths are
influenced by the extent of any deviation from long run
equilibrium. After all, if the system is to return to long
run equilibrium, the movements of at least some of the
variables must respond to the magnitude of the
disequilibrium. (Enders 2004:328). The VECM result is
also sensitive to its lags length. Thus, it is essential
to use appropriate lag length to get the appropriate
outcomes by conducting the lag order selection
criteria (LR, AIC, or SC) tests.
Unlike VAR, cointegration refers to a linear
combination of non-stationary variables. Thus, it is
necessary to test the existence of unit roots in
observed variables using the ADF test as it is used
in VAR. Cointegration also requires that all
variables in a model be integrated of the same
order. Thus, in order to test the existence of
cointegrated variable, one may use the Engle-
Granger (EG) test, which is a residuals-based
approach, or the Johansen Cointegration test. In
the case of a cointegration relationship does not
exist, a VAR analysis in first difference will then be
the correct specification to conduct the estimation
(Enders, 2004:287).
After estimating the VECM equations, the
VEC Pairwise Granger Causality / Block Exogenity
Wald Tests will be applied to reveal whether
changes in one variable cause changes in another.
If so, then lags of variable should be significant in
the equation for the other variable. If this is the
case, it can be said that the variable grangercauses
another.
A direct interpretation of the cointegration
relations may be difficult or misleading (Lutkepohl
and Reimers 1992:53, Runkle 1987:442). As in a
traditional VAR analysis, innovation accounting,
consist of Impulse Response and Variance
Decomposition Analyses, can provide a solution to
the interpretation problem, and might be the most
appropriate method to explain the short run
dynamic structure of market linkages (Yang et al
2003:479). The analysis would give to answers
whether changes in the value of a given variable
have positive or negative effect on other variables
in the system, or how long it would take for the
effect of that variable to work through the system
(Brooks 2002:341).
A shock to the i-th variable not only directly
affects the i-th variable but is also transmitted to
all of the other endogenous variables through the
dynamic (lag) structure of the VAR. An impulse
response function traces the effect of a one-time
shock to one of the innovations on current and
future values of the endogenous variables. In other
words, impulse response analysis will trace out the
responsiveness of the dependent variables in VAR
to shocks on individual error terms. In this paper,
the generalized type of impulse responses analysis
is employed as orthogonalized impulse responses is
sensitive to the ordering of the variable in the
system. The Generalized Impulses as described by
Pesaran and Shin (1998) constructs an orthogonal
set of innovations that does not depend on the VAR
ordering. The generalized impulse responses from
an innovation to the j-th variable are derived by
applying a variable specific Cholesky factor
computed with the j-th variable at the top of the
Cholesky ordering. Dekker, Sen and Young
(2001:31) found that the generalized approach
provided more accurate results than the traditional
orthogonalized approach for both impulse response
and forecast error variance decomposition analysis
Forecast error variance decomposition,
meanwhile, refers to the proportion of the
movements in a sequence due to its own shock
versus shocks to the other variables (Enders
2004:280). This analysis separates the variation in
an endogenous variable into the component shocks
to the system. Thus, the variance decomposition
provides information about the relative importance
of each random innovation in affecting the
variables in the system. It determines how much of
the s-step ahead forecast error variance of a given
variable is explained by innovations to each
explanatory variable. A shock to the i-th variable
will not only affect that variable, but also can be
transmitted to all of the other variables in the
system. To some extent, impulse responses and
variance decompositions offer very similar
information.
EMPIRICAL RESULTS
The Period of before the 2007 Financial
Crisis
The ADF test applied to all variables at level
within the sub-sample period results in acceptence
(fail to reject) of the null hypothesis that the serries
contain unit root. The existence of a unit root in
Asian stock markets, including the ASEAN is well
established in the literature (Masih et al 1999,
2001). The examination then continues to select
the appropriate lag order. The lag orders suggested
by the three lag order selection criteria result in
serially correlated residual. Therefore, as
mentioned by Enders (2004:338), it is necessary to
re-estimate the model using all possible lag length
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
6
until the residual is found serially uncorrelated.
After examination of all possible lag length, the
appropriate lag length is found to be six.
The Johansen Cointegration test then reveals
that there are conflicting results between max
and trace statistic as it is stated in Table 1.
However, as it is suggested by some
econometricians (Johansen and Juselius 1990;
Kasa, 1992; and Serletis and King 1997) that the
trace tends to have more power than the max
because trace takes into account all degrees of
freedom (n-r) of the smallest eigenvalues, then the
number of cointegration vectors suggested by the
trace statistic would be employed. Thus, it may
be concluded that there are two cointegrating
vectors found in the series of the sub-sample period
at 5% level of significance, meaning that the
ASEAN indices are highly interdependent and
significantly related to each other in the long run
during the pre crisis period.
Table 1. The Johansen Cointegration Test For the
sub-sample period of before the 2007
financial Crisis
Trend assumption: Linear deterministic trend
Unrestricted Cointegration Rank Test
Hypothesized
No. of CE(s)
Eigenvalue Trace
Statistic
5 Percent
Critical
Value
1 Percent
Critical
Value
None ** 0.017748 83.02299 68.52 76.07
At most 1 * 0.010864 48.13862 47.21 54.46
At most 2 0.008270 26.85945 29.68 35.65
At most 3 0.004107 10.68322 15.41 20.04
At most 4 0.001368 2.667069 3.76 6.65
Trace test indicates 2 cointegrating equation(s) at the 5% level
Trace test indicates 1 cointegrating equation(s) at the 1% level
Hypothesized
No. of CE(s)
Eigenvalue Max-
Eigen
Statistic
5 Percent
Critical
Value
1 Percent
Critical
Value
None * 0.017748 34.88437 33.46 38.77
At most 1 0.010864 21.27917 27.07 32.24
At most 2 0.008270 16.17623 20.97 25.52
At most 3 0.004107 8.016154 14.07 18.63
At most 4 0.001368 2.667069 3.76 6.65
Max-eigenvalue test indicates 1 cointegrating equation(s) at the
5% level
Max-eigenvalue test indicates no cointegration at the 1% level
The existence of cointegrating vectors resulted
from this study is somewhat consistent with
previous research conducted by Palac-McMiken
(1997:299) and Liu et al (1998:59), but contradicts
with that of Sheng et al (2000:245), in different
period of time. Thus, it can be argued that VECM
is possible to be carried out to estimate the stock
indices of the five ASEAN.
The results of the VECM estimation can be
shown in the two consecutive tables. Table 2
(APPENDIX) shows the estimated cointegrating
vectors, whereas Table 3 report the coefficient of
speed of adjustment.
Table 2. Estimated Cointegrating Vectors
Cointegrating Eq: CointEq1 CointEq2
JAKCOMP 1.000000 0.000000
KLSE 0.000000 1.000000
PSE -2.101383 1.203789
(0.32567) (0.31491)
[-6.45239] [ 3.82264]
SET -0.420384 -0.438546
(0.09796) (0.09472)
[-4.29149] [-4.62993]
STI 1.353018 -2.018991
(0.37949) (0.36695)
[ 3.56532] [-5.50208]
C 1.355101 2.913097
Note: cointegration with unrestricted intercepts and no trends.
Included observations: 1948 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
Table 3. Speed of Adjustment Parameter of the
Error Correction Term (ECT)
Error
Correction:
JAKCOMP
KLSE PSE SET STI
ect1 (α1) -0.004776 5.52E-05 0.009661 0.001528 0.004420
(0.00244) (0.00164) (0.00238) (0.00263) (0.00211)
[-1.95436] [ 0.03365] [ 4.05722] [ 0.58073] [ 2.09935]
ect2 (α2) -0.005994 -0.003282 -0.004991 0.001391 0.005767
(0.00303) (0.00203) (0.00295) (0.00326) (0.00261)
[-1.97914] [-1.61487] [-1.69136] [ 0.42634] [ 2.20980]
Note : cointegration with unrestricted intercepts and no trends
Included observations: 1948 after adjusting endpoints
Standard errors in ( ) & t-statistics in [ ]
As a common practice, Table 2 (APPENDIX)
shows that the first cointegrating vector is
normalized by JAKCOMP, while KLSE is
restricted to zero. Meanwhile, in the second one,
KLSE is used to normalize, while JAKCOMP is
restricted to zero. Based on t-statistic at the 5%
level of significance, JAKCOMP, PSE, SET, and
STI are found significant in the first cointegration
vector, while KLSE, PSE, SET, and STI are
significant in the second one. This means that all of
the significant indices (variables) significantly
contribute to the ASEAN indices’ long run
equilibrium.
With the same critical value of 5%, the speed of
adjustment coefficient for the first and second
cointegrating vector, for KLSE and SET are
statistically zero. This implies that both vectors
have no contribution to the convergence of these
indices to their long run paths, although SET does
have significant influence on any of the
cointegrating relationship, and KLSE affects only
the second one.
Atmadja: The Asean Stock Market Integration
7
In contrast, the speed of adjustment of
JAKCOMP, PSE, and STI are statistically
significant in both vectors. JAKCOMP has
negative influences in both cointegrating
relationship indicating a downward long run
adjustment. Conversely, STI affects the vectors
positively implying an upward long run
adjustment. In the second cointegrating vector,
JAKCOMP will react to a disequilibrium among
KLSE, PSE, SET, and STI. Thus, the vector would
contribute to the convergence of JAKCOMP to its
long run path, even though the index does not have
any significant contribution to the others return to
the long run equilibrium. PSE interestingly has
positive and negative significant impact on the first
and the second cointegration vectors, respectively.
The implication is that PSE would react positively
in the first vector, and negatively in the second one.
The existence of the cointegrating relationship
in the region during the time period could be
caused by some reasons. First, the degree of
economic integration in the ASEAN countries has
risen after the 1997 financial crisis. The
information barriers have also significantly decline
as a result of technological advance in IT
(information technology) and in the markets’
trading operating systems. The other reason is that
the degree of institutionalization and securitization
have increased in the regional market promoting
intra-regional fund transfers to increase
diversification opportunities.
After the VECM estimation is determined, the
next step is to search the existence of granger
causality among variables of each model. The
results of VEC Pairwise Granger Causality Tests
for each country are presented Table 4. Using a 5%
level of significance, the table shows only four
significant causality linkages found among the
variables in the pre crisis period. It also reveals
that none of the other ASEAN indices is
significantly granger caused JAKCOMP during the
period, vice versa. Thus, it may be concluded that
movements of the index during the period
apparently become isolated from the influence of
the others. STI experienced almost the same
condition as JAKCOMP when all other ASEAN
indices do not granger cause the index. However,
somewhat different with JAKCOMP, STI, as well
as SET, granger cause (in uni-directional form)
KLSE meaning that movements in KLSE
appeared to lag those of STI and SET. Moreover,
SET also appears to have bi-directional causality
with PSE.
As a part of the Accounting Innovation
Analysis, the impulse response analysis traces out
the responsiveness of the dependent variable in the
system to shocks to each of the variables (Brooks,
2002:341). The generalized type of the impulse
response analysis will be applied in this study to
observe short run dynamic interactions among the
variables, since orthogonalized impulse responses
is sensitive to the ordering of the variable in the
system. The complete result of the analysis is
presented in Table 5.
Table 4. VEC Pairwise Granger Causality/Block
Exogeneity Wald Tests
Dependent
variable Exclude Chi-sq Prob.
JAKCOMP KLSE 6.158533 0.4057
PSE 10.79299 0.0950
SET 9.533360 0.1457
STI 7.470766 0.2795
KLSE JAKCOMP 4.013962 0.6748
PSE 11.10882 0.0851
SET 12.83167 0.0458
STI 18.49538 0.0051
PSE JAKCOMP 12.17061 0.0583
KLSE 4.755074 0.5756
SET 40.46320 0.0000
STI 10.91111 0.0912
SET JAKCOMP 4.591306 0.5972
KLSE 10.01266 0.1241
PSE 13.22751 0.0396
STI 4.343344 0.6303
STI JAKCOMP 12.14867 0.0587
KLSE 9.910047 0.1285
PSE 8.841288 0.1827
SET 9.852119 0.1310
Table 5. The Impulse Response to Generalized One
S.D. Innovations
Response
of
Period JAKCOMP
KLSE PSE SET STI
JAKCOMP 1 0.012863 0.003140 0.002791 0.003167 0.004256
2 0.013992 0.003609 0.003377 0.003920 0.005196
3 0.013608 0.004138 0.003762 0.004475 0.005381
4 0.013862 0.004212 0.004669 0.005340 0.006202
5 0.014158 0.004193 0.005338 0.005551 0.006843
6 0.014500 0.004412 0.005403 0.006451 0.007608
7 0.014227 0.004330 0.005254 0.006773 0.007709
KLSE 1 0.002107 0.008631 0.001743 0.002438 0.003392
2 0.002839 0.010238 0.001909 0.003186 0.004508
3 0.002793 0.010509 0.001585 0.003610 0.004519
4 0.003137 0.010794 0.001569 0.003572 0.005168
5 0.003293 0.010699 0.001611 0.003504 0.005511
6 0.003476 0.010717 0.001286 0.003923 0.005946
7 0.003373 0.010630 0.001105 0.003980 0.005859
PSE 1 0.002720 0.002532 0.012534 0.002354 0.002792
2 0.004037 0.003496 0.013744 0.004353 0.004535
3 0.003873 0.003554 0.013203 0.004698 0.004649
4 0.004664 0.003473 0.012587 0.005219 0.005467
5 0.004522 0.003449 0.012576 0.005347 0.005576
6 0.004933 0.003293 0.011914 0.006350 0.005723
7 0.005139 0.003108 0.011751 0.006768 0.005631
SET 1 0.003410 0.003914 0.002602 0.013853 0.005060
2 0.003756 0.004555 0.003256 0.013892 0.005480
3 0.003845 0.004985 0.003782 0.014790 0.006311
4 0.004091 0.005459 0.004515 0.014805 0.006699
5 0.003881 0.004818 0.004692 0.014721 0.006660
6 0.003922 0.005431 0.004932 0.015535 0.007066
7 0.003364 0.004790 0.004150 0.014909 0.007037
STI 1 0.003667 0.004355 0.002469 0.004048 0.011083
2 0.003284 0.003814 0.002652 0.004329 0.011242
3 0.002991 0.003888 0.002978 0.004751 0.011037
4 0.003094 0.003998 0.002944 0.004987 0.011528
5 0.002847 0.004416 0.003423 0.005319 0.012042
6 0.002970 0.004458 0.003290 0.005579 0.012248
7 0.002747 0.004464 0.003485 0.005614 0.011606
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As can be seen in Table 5, a generalised
impulse response analysis indicates that one
standard error shock to JAKCOMP would result in
a positive response by changes in STI of 0.0037,
one step ahead. Afterward, the responses have
become smaller ever since. A shock to STI,
commonly believed as the most prominent stock
index in ASEAN, results in second greatest
changes in the other indices in the short run
period. Meanwhile, the greatest contemporaneous
reaction of an index generally due to its own
shocks. This indicates that internal/domestic
shocks in a particular index may have greatest
significant impacts on its movements, and STI
become the most influential stock index in the
region at the time period.
While impulse response functions trace the
effects of a shock to one endogenous variable on to
the other variables in the system, variance
decomposition separates the variation in an
endogenous variable into the component shocks to
the system. As it is mentioned by Enders
(2004:280) the forecast error variance
decomposition tells the proportion of the
movements in a sequence due to its own shock
versus shock to the other variable. A shock to the ith
variable will not only affect that variable, but
can also be transmitted to all of the other variables
in the system.
Table 6 presents the result of the forecast error
variance decomposition of the serries in the period
of before financial crisis. As can be seen from the
table, in general, the proportion movements of the
indices are dominantly due to their own shocks.
Surprisingly, only around 70% of the error variance
of STI was attributable to own shocks in the steps
ahead, while JAKCOMP contributed maximum of
11% to STI’s error variance.
The Period of the 2007 Financial Crisis
The ADF test conducted to the serries at level
reveals the presence of unit root in the serries. The
lags order test then shows three lags length as the
appropriate lag order since the residual is not
serially correlated. However, the Johansen
Cointegration test fails to find the existence of
cointegration vector in the serries. This concludes
that the serries has no cointegrating relationship.
In other words, the indices have no long run
equilibrium during the 2007 financial crisis. The
finding somewhat contradicts with the ones given
by some other researchers (Arshanapalli et al 1993;
Sheng et al 2000, and Yang et al 2003), but
confirms that of Roca (2000:145).
The absence of cointegrating vector in the
series indicates that the cointegration analysis
framework is not possible to be carried out. Hence,
the VAR analysis framework would be applied to
estimate the relationship of the indices, as well as
to reveal the short run dynamic interactions among
the indices.
Table 6. Variance Decomposition
Variance Decomposition of JAKCOMP:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.012863 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.019020 99.85659 0.010939 0.028547 0.049753 0.054174
3 0.023434 99.50386 0.136144 0.109426 0.186402 0.064169
4 0.027354 98.70463 0.197381 0.416767 0.512016 0.169204
5 0.030979 97.84212 0.214017 0.842533 0.727739 0.373589
6 0.034447 96.85652 0.241250 1.088327 1.165416 0.648487
7 0.037525 95.98986 0.258696 1.232197 1.641083 0.878166
Variance Decomposition of KLSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.008631 5.960468 94.03953 0.000000 0.000000 0.000000
2 0.013407 6.954068 92.87426 0.028773 0.040597 0.102303
3 0.017064 6.971806 92.59963 0.141095 0.195879 0.091595
4 0.020238 7.359674 91.94159 0.229436 0.209507 0.259789
5 0.022955 7.778958 91.22487 0.270537 0.201897 0.523735
6 0.025444 8.198359 90.24934 0.397181 0.289759 0.865363
7 0.027681 8.411056 89.59566 0.531422 0.381012 1.080849
Variance Decomposition of PSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.012534 4.709320 2.360945 92.92974 0.000000 0.000000
2 0.018719 6.761489 2.970640 89.41751 0.670515 0.179843
3 0.023044 7.286824 3.322941 87.93643 1.180083 0.273723
4 0.026505 8.605063 3.336361 85.70276 1.768701 0.587110
5 0.029573 9.250590 3.348796 84.34696 2.224120 0.829537
6 0.032241 10.12412 3.263569 82.40609 3.231500 0.974721
7 0.034710 10.92704 3.118762 80.65164 4.273855 1.028701
Variance Decomposition of SET:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.013853 6.060589 5.260581 1.058338 87.62049 0.000000
2 0.019639 6.673187 6.267257 1.496936 85.55756 0.005057
3 0.024625 6.681866 6.857899 1.896486 84.48702 0.076732
4 0.028819 6.893286 7.554222 2.489728 82.92112 0.141642
5 0.032441 6.871721 7.475509 3.036180 82.40051 0.216083
6 0.036054 6.746606 7.689571 3.399525 81.89916 0.265139
7 0.039080 6.483311 7.641728 3.442428 82.03551 0.397027
Variance Decomposition of STI:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.011083 10.94870 10.36302 1.108366 4.525942 73.05397
2 0.015812 9.692145 8.950799 1.464531 5.434735 74.45779
3 0.019334 8.876028 8.824737 1.942618 6.518288 73.83833
4 0.022557 8.401438 8.680124 2.081363 7.188879 73.64820
5 0.025643 7.733370 8.956077 2.414139 7.690111 73.20630
6 0.028485 7.354068 9.084298 2.515628 8.205909 72.84010
7 0.030842 7.066422 9.358043 2.738126 8.729031 72.10838
The VAR analysis, however, requires that the
series must be stationary. Hence, the non
stationary series may be made stationary by
differencing or detrending process. After
transforming the serries into first difference form,
the ADF test is re-employed to ensure that the
series are now stationary. The lag order test then
indicated that the appropriate lag length would be
three. After estimating the series using the VAR in
first difference analysis, the estimated models can
be shown in Table 7.
Table 8 shows the results of a block causality
test implemented on the series. The table reveals
that, using a 5 % level of significance, more
variables significantly granger cause another in the
crisis period compared to those in pre crisis period.
It means that there are more variables that their
current values have correlation with the past (lags)
value of another implying that the present value of
an index can be predicted with greater accuracy by
using past value of another. This then indicates
that there is an increase in causal linkages among
Atmadja: The Asean Stock Market Integration
9
those indices in the region during the crisis period.
The results are in fact different with those before
the crisis period showing a changing behaviour in
the indices’ movements. For instance, the lags of
SET and STI now significantly enter into the
equation for JAKCOMP, while in the pre crisis
period does not.
Table 7. Vector Autoregression Estimates
JAKCOMP KLSE PSE SET STI
JAKCOMP(-1) 0.072296 0.134318 0.132358 0.101597 0.029438
(0.06530) (0.03643) (0.05607) (0.05610) (0.06375)
[ 1.10716] [ 3.68735] [ 2.36041] [ 1.81101] [ 0.46179]
JAKCOMP(-2) 0.120791 0.083802 0.143389 0.198738 0.096179
(0.06664) (0.03718) (0.05723) (0.05726) (0.06506)
[ 1.81247] [ 2.25413] [ 2.50552] [ 3.47108] [ 1.47829]
JAKCOMP(-3) -0.058141 -0.010836 -0.017686 -0.026591 0.005682
(0.06641) (0.03705) (0.05703) (0.05706) (0.06484)
[-0.87543] [-0.29248] [-0.31011] [-0.46604] [ 0.08764]
KLSE(-1) -0.208902 -0.185895 -0.070152 -0.186469 -0.231196
(0.10815) (0.06033) (0.09287) (0.09291) (0.10558)
[-1.93162] [-3.08130] [-0.75537] [-2.00693] [-2.18980]
KLSE(-2) -0.210401 -0.180766 -0.102958 -0.375966 -0.275983
(0.10879) (0.06069) (0.09342) (0.09346) (0.10620)
[-1.93407] [-2.97869] [-1.10212] [-4.02270] [-2.59867]
KLSE(-3) 0.062985 0.124513 0.038155 0.027387 0.117089
(0.10840) (0.06047) (0.09308) (0.09313) (0.10582)
[ 0.58106] [ 2.05914] [ 0.40990] [ 0.29409] [ 1.10649]
PSE(-1) 0.017456 0.029010 -0.043004 0.048177 0.012092
(0.06255) (0.03489) (0.05371) (0.05374) (0.06106)
[ 0.27907] [ 0.83140] [-0.80062] [ 0.89653] [ 0.19802]
PSE(-2) 0.051068 0.048802 0.014394 0.077747 0.058784
(0.06224) (0.03472) (0.05345) (0.05347) (0.06076)
[ 0.82050] [ 1.40558] [ 0.26931] [ 1.45401] [ 0.96747]
PSE(-3) -0.020801 -0.019708 -0.040438 0.010962 -0.049437
(0.06019) (0.03358) (0.05168) (0.05171) (0.05876)
[-0.34561] [-0.58698] [-0.78239] [ 0.21200] [-0.84138]
SET(-1) 0.135694 -0.014998 0.029879 -0.069210 -0.078839
(0.07317) (0.04082) (0.06283) (0.06286) (0.07143)
[ 1.85453] [-0.36744] [ 0.47554] [-1.10100] [-1.10371]
SET(-2) -0.043142 0.019411 -0.125749 0.016529 0.040208
(0.07254) (0.04047) (0.06229) (0.06232) (0.07082)
[-0.59473] [ 0.47969] [-2.01868] [ 0.26523] [ 0.56778]
SET(-3) -0.149188 -0.042758 -0.077355 -0.060061 -0.147843
(0.07241) (0.04039) (0.06218) (0.06221) (0.07069)
[-2.06042] [-1.05858] [-1.24410] [-0.96552] [-2.09154]
STI(-1) 0.151469 0.077991 0.230158 0.060714 0.124110
(0.07280) (0.04061) (0.06252) (0.06255) (0.07107)
[ 2.08051] [ 1.92034] [ 3.68142] [ 0.97068] [ 1.74621]
STI(-2) 0.048648 0.026292 0.062191 0.044742 0.028764
(0.07342) (0.04096) (0.06305) (0.06308) (0.07168)
[ 0.66258] [ 0.64191] [ 0.98637] [ 0.70931] [ 0.40130]
STI(-3) 0.155315 -0.012852 0.046721 0.128301 0.021717
(0.07118) (0.03971) (0.06113) (0.06116) (0.06949)
[ 2.18190] [-0.32366] [ 0.76432] [ 2.09796] [ 0.31251]
C -0.000179 -0.000638 -0.000853 -0.000801 -0.001001
(0.00093) (0.00052) (0.00080) (0.00080) (0.00091)
[-0.19335] [-1.23409] [-1.07116] [-1.00633] [-1.10644]
Note: Standard errors in ( ) & t-statistics in [ ]
5 % level of significant
Table 8. VAR Pairwise Granger Causality/Block
Exogeneity Wald Tests
Dependent
variable Exclude Chi-sq Prob.
JAKCOMP KLSE 7.633367 0.0542
PSE 0.892896 0.8271
SET 7.966430 0.0467
STI 9.699188 0.0213
KLSE JAKCOMP 18.50092 0.0003
PSE 3.030814 0.3869
SET 1.542379 0.6725
STI 4.229433 0.2377
PSE JAKCOMP 11.80429 0.0081
KLSE 1.972551 0.5781
SET 5.832183 0.1201
STI 15.30169 0.0016
SET JAKCOMP 15.51245 0.0014
KLSE 19.47387 0.0002
PSE 2.766382 0.4291
STI 5.971454 0.1130
STI JAKCOMP 2.368227 0.4996
KLSE 12.88606 0.0049
PSE 1.790673 0.6170
SET 6.127043 0.1056
In order to capture the short run dynamic
interaction among the variables during the
financial crisis period, the generalized impulse
response and the forecast error variance
decomposition, would also be employed. The results
of the generalized impulse response analysis of the
series are presented in Table 9. As it is shown in
the table, during the financial crisis, the
generalised impulse response analysis indicates
that all variables gave greater immediate reactions
to a shock of one variable compared to those in the
pre-crisis era. This implies that the short run
interaction between two indices became more
intense during the 2007 financial crisis period. In
other words, the findings strongly indicate that the
ASEAN indices become more interdependent
during the financial crisis, although they had no
long run equilibrium.
The variance decomposition analysis (Tabel
10) reveals that the proportion of the movements in
an index due to its own shock for all indices
declined during the financial crisis. This means
that in the period of the financial crisis shocks to
other indices have more explanatory power to the
movements of a particular index in the s-steps
ahead. This finding seems reinforce the result of
generalized impulse response analysis that during
the 2007 financial crisis period, the ASEAN’s stock
indices tend to be more interdependent. Thus, it
somewhat confirmed the previous researches done
by Roca (2000:145) and Yang et al (2003:478)
which conclude that interdependency and causal
linkages among the indices become more
significant during crisis.
JURNAL AKUNTANSI DAN KEUANGAN, VOL. 11, NO. 1, MEI 2009: 1-12
10
Table 9. The Impulse Response to Generalized
One S.D. Innovations
Response
of
Period JAKCOMP KLSE PSE SET STI
JAKCOMP 1 0.020461 0.011579 0.008953 0.011621 0.013632
2 0.003635 0.001526 0.002198 0.004214 0.004293
3 0.001716 -0.000584 0.000966 0.000523 0.001221
4 0.000100 0.000434 -0.000181 -0.000541 0.001408
5 -0.000507 -0.000267 -0.000492 -0.000799 -0.000178
6 -0.000571 -0.000327 -0.000373 -0.000433 -0.000329
7 -0.000532 -0.000227 -0.000441 -0.000583 -0.000670
KLSE 1 0.006459 0.011414 0.005095 0.005452 0.006781
2 0.002659 0.000461 0.001297 0.001534 0.002167
3 0.001598 -0.000120 0.001140 0.001414 0.001395
4 -4.01E-05 0.000663 -0.000214 -0.000399 5.15E-05
5 8.40E-05 -0.000142 -0.000101 -0.000276 0.000126
6 -0.000133 -7.84E-05 -8.03E-05 -7.53E-05 8.39E-05
7 -0.000223 -3.72E-05 -0.000149 -0.000202 -0.000250
PSE 1 0.007689 0.007843 0.017570 0.007738 0.007307
2 0.005286 0.003377 0.002215 0.004376 0.005958
3 0.002075 -2.87E-05 0.000618 -4.34E-05 0.001447
4 -0.000102 -0.000320 -0.000544 -0.000358 0.000359
5 -0.000711 -0.000156 -0.000596 -0.000995 -0.000434
6 -0.000525 -0.000297 -0.000408 -0.000535 -0.000410
7 -0.000345 -0.000107 -0.000214 -0.000261 -0.000347
SET 1 0.009984 0.008397 0.007742 0.017578 0.011578
2 0.001362 -0.000435 0.000775 0.000119 0.000884
3 0.003034 -0.000533 0.001747 0.002050 0.002101
4 0.000804 0.001367 0.000655 0.000834 0.002134
5 -0.000160 -0.000455 -0.000346 -0.000645 -0.000140
6 -0.000169 -0.000252 -0.000108 -0.000116 0.000174
7 -0.000474 -0.000120 -0.000405 -0.000544 -0.000503
STI 1 0.013308 0.011867 0.008307 0.013156 0.019974
2 6.65E-05 -0.001392 -0.000281 -0.000578 0.000488
3 0.000879 -0.001056 0.000734 0.000894 0.000598
4 -0.001135 0.000113 -0.001453 -0.002127 -0.000913
5 -0.000329 -0.000385 -0.000354 -0.000535 -0.000465
6 -0.000392 -5.06E-05 -0.000155 -9.08E-05 -8.33E-05
7 -0.000300 -0.000110 -0.000214 -0.000306 -0.000493
Table 10. Variance Decomposition
Variance Decomposition of JAKCOMP:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.020461 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.021073 97.24338 0.093183 0.174688 1.656038 0.832708
3 0.021264 96.15798 0.878554 0.303776 1.644745 1.014946
4 0.021412 94.83229 0.912108 0.332163 1.758313 2.165129
5 0.021436 94.68301 0.910264 0.353508 1.830443 2.222772
6 0.021445 94.67600 0.909518 0.357529 1.831413 2.225544
7 0.021458 94.61447 0.910093 0.372423 1.851690 2.251322
Variance Decomposition of KLSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.011414 32.02605 67.97395 0.000000 0.000000 0.000000
2 0.011842 34.79170 64.28911 0.182911 0.037178 0.699104
3 0.012084 35.16451 62.80328 0.676161 0.434642 0.921404
4 0.012135 34.86724 62.73966 0.813231 0.661356 0.918514
5 0.012148 34.79750 62.64135 0.817565 0.737312 1.006279
6 0.012152 34.78759 62.60206 0.817460 0.736878 1.056009
7 0.012156 34.79723 62.56663 0.822289 0.743584 1.070267
Variance Decomposition of PSE:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.017570 19.14909 5.811252 75.03966 0.000000 0.000000
2 0.018664 24.99113 5.212981 66.52072 0.824013 2.451155
3 0.018915 25.53371 5.670186 64.76357 1.220127 2.812400
4 0.018949 25.44521 5.678093 64.59816 1.227054 3.051487
5 0.018986 25.48622 5.680653 64.39330 1.373668 3.066158
6 0.018997 25.53520 5.674576 64.33616 1.390703 3.063358
7 0.019001 25.55543 5.674949 64.30681 1.392098 3.070709
Variance Decomposition of SET:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.017578 32.25784 3.592081 2.848586 61.30150 0.000000
2 0.017729 32.29985 4.211044 2.919796 60.38029 0.189017
3 0.018269 33.17686 6.197357 3.213120 57.06075 0.351916
4 0.018426 32.80436 6.452377 3.159159 56.10525 1.478857
5 0.018446 32.74148 6.496111 3.163665 56.07662 1.522124
6 0.018455 32.71765 6.500189 3.160603 56.02145 1.600115
7 0.018468 32.73729 6.500537 3.178835 55.97476 1.608586
Variance Decomposition of DLNSTI:
Period S.E. JAKCOMP KLSE PSE SET STI
1 0.019974 44.38852 6.931322 0.493399 7.788191 40.39857
2 0.020116 43.76814 7.577686 0.490308 7.717361 40.44651
3 0.020262 43.32822 8.333029 0.692744 7.747659 39.89834
4 0.020443 42.86986 8.386302 1.122186 8.404362 39.21729
5 0.020451 42.86178 8.393528 1.128666 8.426711 39.18932
6 0.020457 42.87448 8.399131 1.128383 8.425800 39.17221
7 0.020464 42.86514 8.394334 1.130788 8.426286 39.18345
CONCLUSION
The study concludes that two cointegrating
vectors are found in the series before the 2007
financial crisis period indicating the existing of long
run equilibrium in the series during the time
period. However, the study fails to find any
cointegrating vector in the series during the
financial crisis period. The results prove that the
long run relationship of the ASEAN indices has
been removed by the 2007 financial crisis.
The block causality tests employed in both subsample
period reveal that more significant causal
linkages are found in the series during the
financial crisis period compared to those before the
financial crisis. The accounting innovation
analyses conducted to the series also indicate that
the short run dynamic interactions among the
indices tend to be more intense during the financial
crisis period. These all indicate that the indices
become more interdependent during the financial
crisis period since the moment gives rise the
explanatory power of a sequence to the movements
of another.
The general conclusion that may be withdrawn
from this study is that the contagious effect of the
2007-US financial crisis has affected the ASEAN’s
capital market integration, and has changed the
behaviour of the indices’ movements both in the
short run and in the long run.
Thus, the implication policy that can be
suggested is that the diversification of portfolio
within the ASEAN stock markets in the short run
is unlikely to reduce the risk due to the high degree
of financial interdependent of these markets
during the financial crisis.
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Vancouver, http://www.cfapubs.org.

jurnal akuntansi 2

Mei 11, 2010

Pengaruh Tingkat Pendidikan, Pengalaman Bekerja, Kecakapan Profesional, Independensi Pemeriksa Terhadap Kualitas Hasil Pemeriksaan (Studi Empiris : Badan Pengawas Daerah Kabupaten Karo)

IYOS ANDERSEN BANGUN
Alumni Fakultas Ekonomi Universitas Sumatera Utara

ARIFIN LUBIS
Universitas Sumatera Utara

The purpose of this research was to show the effect of Education Grade, Continuing Education, and Independency of Auditor to the Quality of the Inspection’s Result (Empiric study : Badan Pengawasan Daerah in Karo’s Regency).
Independent variables in this study were Education Grade, working Experience, Skilfull Professionalism and Independency of Auditor. Dependent variable in this study was Quality of the Inspection’s Result. The data in this study was the primary data that has obtained from the spreading questionnair directly to all of responden. The population and samples that used in this research were the staf of Badan Pengawasan Daerah in Karo’s Regency. The method of research were descriptive analysis, validity and reliability test, multiple regression analysis with identification test. The analyzing method used statistic method with SPSS 12.
The result of research showed that Education Grade, working experience, skillful professionalism and Independency of Auditor were simultaneous affected significantly to the Quality of the Inspection’s Result at Badan Pengawasan Daerah in Karo’s Regency. However, partially, Education Grade, Working Experience and Independency Of auditor didn’t affect significantly to the Quality of the Inspection’s Result at Badan Pengawasan Daerah in Karo’s Regency. Skillful Professionalism dominant affected significantly to the Quality of the Inspection’s Result at Badan Pengawasan Daerah in Karo’s Regency.

Keywords : Education Grade, Working Experience, Skillful Professionalism Independency of Auditor, Quality of the Inspection’s Result.

1. Pendahuluan

Diberlakukannya Undang-Undang Nomor 22 Tahun 1999 jo. Undang-Undang Nomor 32 Tahun 2004 tentang Pemerintah Daerah dan Undang-Undang Nomor 25 tahun 1999 jo. Undang-Undang Nomor 33 Tahun 2004 tentang Perimbangan Keuangan Antara Pemerintah Pusat dan Daerah: “Otonomi daerah adalah hak wewenang dan kewajiban daerah untuk mengatur dan mengurus rumah tangganya sendiri sesuai dengan peraturan perundang-undangan yang berlaku”. Hal ini tentunya membawa perubahan juga terhadap pengelolaan keuangan (fiskal) negara sehubungan dengan penyerahan kewenangan dari pemerintah pusat kepada pemerintah daerah sehingga pemerintah daerah mengatur sendiri mengenai pengelolaan keuangan daerahnya.
Dengan adanya otonomi daerah maka praktis bentuk dan struktur pemerintah daerah diseluruh Indonesia adalah sama termasuk lembaga pengawasan fungsional di propinsi Indonesia disebut dengan inspektorat wilayah provinsi yang ditetapkan dalam Keputusan Menteri Dalam Negeri Nomor 110 Tahun1991. Inspektorat Wilayah Propinsi adalah aparat pengawasan fungsional yang taktis operasional berada dibawah dan bertanggung jawab kepada Gubernur Kepala Daerah Tingkat 1 dan Teknis Administratif dibawah Menteri dalam Negeri. Lembaga pengawasan fungsional berguna untuk meningkatkan akuntabilitas pengelolaan keuangan negara yang bertujuan untuk mewujudkan tata pemerintahan yang baik.
Kurang tegas perbedaan unsur perencana, pelaksana, dan pengawas di daerah dimana Bawasda tersebut sebagai lembaga teknis sama dengan badan lainnya seperti badan Diklat, Bappeda, padahal Bawasda adalah lembaga pengawas terhadap lembaga teknis, pelaksana, dan perencana. Berdasarkan PP 84 Tahun 1999 dan PP 8 Tahun 2003 tentang struktur organisasi pemerintah menempatkan Bawasda pada posisi kurang Independen terhadap hasil pemeriksaannya. Hal ini disebabkan karena Bawasda dalam melaporkan hasil pengawasannya kepada Bupati harus melalui sekretariat daerah, padahal sekretariat daerah adalah objek pemeriksaan Bawasda. Kondisi seperti ini memberikan peluang mengintervensi hasil pengawasan dan pemeriksaan, hal ini juga tidak sesuai dengan prinsip prinsip Akuntansi.
Tujuan penulis melakukan penelitian adalah untuk mengetahui pengaruh tingkat pendidikan, pengalaman bekerja, kecakapan profesional dan independensi pemeriksa secara terpisah maupun terpadu terhadap kualitas hasil pemeriksaan. Penulis tertarik untuk memilih judul “Pengaruh Tingkat Pendidikan, Pengalaman Bekerja, Kecakapan Profesional, Independensi Pemeriksa Terhadap Kualitas Hasil Pemeriksaan” karena meilihat pentingnya pengaruh tingkat pendidikan, pengalaman bekerja kecakapan professional dan independensi pemeriksa terhadap hasil pemeriksaan badan pengawas daerah suatu daerah demi terciptanya suatu laporan yang akuntabel dan dapat dipertanggungjawabkan.

2. Tinjauan Pustaka
2.1 Tingkat Pendidikan
Pemeriksa dituntut harus memiliki wawasan yang luas dan mendalam atas segala kegiatan yang akan diperiksanya. Namun pada kenyataannya sebagian besar dari pemeriksa pada badan pengawas daerah masih belum memiliki kompetensi untuk melakukan pemeriksaan, hal ini mengakibatkan pemeriksaan yang dilakukan badan pengawas tidak efektif dan efisien. Salah satu penyebab utamanya adalah tingkat pendidikan yang tidak merata dan beraneka ragam latar belakang jurusan pendidikan.

2.2 Pengalaman Bekerja
Pengalaman audit adalah pengalaman auditor dalam melakukan audit laporan keuangan baik dari segi lamanya waktu maupun banyaknya penugasan yang pernah ditangani, semakin banyak pengalaman auditor semakin dapat menghasilkan berbagai macam dugaan dalam menjelaskan temuan audit. Akuntan pemeriksa yang berpengalaman akan membuat judgment yang relatif lebih baik dalam tugas-tugas profesional ketimbang akuntan pemeriksa yang belum berpengalaman, dan mampu mengidentifikasi secara lebih baik mengenai kesalahan-kesalahan dalam telaah analitik .Pengetahuan auditor tentang audit akan semakin berkembang dengan bertambahnya pengalaman bekerja

2.3 Kecakapan Profesional
Dalam Peraturan Badan Pemeriksa Keuangan Republik Indonesia No 01 Tahun 2007 tentang standar pemeriksaan keuangan dinyatakan “Pemeriksa secara kolektif harus memiliki kecakapan professional yang memadai untuk melakukan tugas pemeriksaan”. Dengan pernyataan ini semua organisasi pemeriksa bertanggung jawab untuk memastikan bahwa setiap pemeriksaan dilaksanakan oleh para pemeriksa yang secara kolektif memiliki pengetahuan, keahlian dan pengalaman yang dibutuhkan untuk melaksanakan tugas tersebut.
Kecakapan profesional adalah kemampuan dan keahlian spesifik pada bidang-bidang tertentu yang telah dipilih seseorang. Kecakapan tidak cukup hanya “mampu mengerjakan” tetapi juga memiliki kemampuan “memecahkan masalah” (trouble shooting) di bidangnya tersebut. Hal ini memungkinkan auditor untuk dengan cepat dan cekatan mengembangkan dan memperagakan pengetahuan kerja yang baru dan berbeda dalam kaitannya dengan persoalan, orang-orang dan situasi kerja.

2.4 Independensi Pemeriksa
Dalam Peraturan Badan Pemeriksa Keuangan Republik Indonesia No 01 Tahun 2007 tentang standar pemeriksaan keuangan dinyatakan bahwa “Dalam semua hal yang berkaitan dengan pekerjaan pemeriksaan,organisasi pemeriksa dan pemeriksa harus bebas dalam sikap mental dan penampilan pribadi dari gangguan pribadi, ekstern dan organisasi yang dapat mempengaruhi independensinya”.
Pemeriksa perlu menghindar dari situasi yang menyebabkan pihak ketiga yang menetahui fakta dan keadaan yang relevan menyimpulkan bahwa pemeriksa tidak dapat mempertahankan independensinya sehingga tidak mampu memberikan penilaian yang objektif dan tidak memihak terhadap semua hal yang terkait dalam pelaksanaan dan pelaporan hasil pemeriksaan.

2.5 Kualitas Hasil Pemeriksaan
Dalam Peraturan Badan Pemeriksa Keuangan Republik Indonesia Nomor 01 Tahun 2007 tentang Standar Pemeriksaan Keuangan Negara, dijelaskan tentang Laporan Hasil Pemeriksaan (LHP) yang merupakan bagian dari kualitas hasil pemeriksaan.
Laporan Hasil Pemeriksaan yang memuat adanya kelemahan dalam pengendalian intern, kecurangan, penyimpangan dari ketentuan dalam pengendalian peraturan perundang-undangan, dan ketidakpatutan, harus dilengkapi tanggapan dari pimpinan atau pejabat yang bertanggung jawab pada entitas yang diperiksa mengenai temuan dan rekomendasi serta tindakan koreksi yang direncanakan.

Pelaksanaan pemeriksaan yang didasarkan pada Standar Pemeriksaan akan meningkatkan kredibilitas informasi yang dilaporkan atau diperoleh dari entitas yang diperiksa melalui pengumpulan dan pengujian bukti secara obyektif. Apabila pemeriksa melaksanakan pemeriksaan dengan cara ini dan melaporkan hasilnya sesuai dengan Standar Pemeriksaan maka hasil pemeriksaan tersebut akan dapat mendukung peningkatan mutu pengelolaan dan tanggung jawab keuangan Negara serta pengambilan keputusan Penyelenggara Negara.

2.6 Tinjauan Penelitian Terdahulu
Hasil penelitian Surbakti Karo-Karo (2006) menyimpulkan bahwa kompetensi Anggota Badan Pengawas mempunyai pengaruh secara signifikan terhadap Laporan Badan Pengawas. Variabel Latar Belakang Pendidikan mempunyai nilai paling tinggi. Variabel Pengalaman mempunyai nilai paling rendah.
Hasil penelitian Rizal Iskandar Batubara (2008) menyimpulkan bahwa latar Belakang Pendidikan, Kecakapan Profesional, Pendidikan Berkelanjutan, dan Independensi Pemeriksa secara simultan mempunyai pengaruh secara signifikan terhadap Kualitas Hasil Pemeriksaan. Variabel Independensi Pemeriksa mempunyai nilai paling tinggi. Variabel Latar Belakang Pendidikan secara parsial tidak mempunyai pengaruh secara signifikan terhadap Kualitas Hasil Pemeriksaan.

2.7 Kerangka Konseptual
Kerangka konseptual merupakan sintesis dari tinjauan teoritis yang mencerminkan keterkaitan antar variable yang diteliti dan merupakan tuntunan untuk memecahkan masalah penelitian.
Kualitas hasil pemeriksaan suatu laporan keuangan dipengaruhi oleh beberapa factor antara lain adalah :
Tingkat Pendidikan : tingkat pendidikan merupakan tingkat atau strata pendidikan serta jurusan pendidikan yang dimiliki oleh staf Badan Pengawas Daerah Kabupaten Karo, semakin terfokusnya tingkat pendidikan seorang pemeriksa pada bidang pemeriksaan tentu kualitas hasil semakin baik. Tingkat Pendidikan berdasarkan penelitian sebelumnya berpengaruh positif terhadap Kualitas hasil pemeriksaan. Namun pada penelitian ini memeiliki pengaruh negative.
Pengalaman Bekerja : merupakan pengalaman yang dimiliki staf Badan Pengawas Daerah Karo dalam melakukan pemeriksaan, semakin lama seorang staf bertugas sebagai badan pengawas akan menambah keahlian dalam menghadapi masalah masalah yang terjadi saat melakukan pemeriksaan, dimana hal inin jugaakan mempengaruhi kualitas hasil pemeriksaan dari suatu laporan. Pada penelitian sebelumnya pengalaman bekerja memiliki pengaruh positif terhadap Kualitas hasil pemeriksaan.
Kecakapan Profesional : dilihat dari pengetahuan dan keahlian staf Badan Pengawas Daerah Karo. Dimana pemeriksa yang ditugaskan untuk melaksanakan pemeriksaan keuangan secara kolektif harus memiliki keahlian yang dibutuhkan serta memiliki sertifikasi keahlian yang diterima umum, semakin cakap seorang pemeriksa pada bidang pemeriksaan maka kualitas hasil pemeriksaan akan semakion baik.
Independensi Pemeriksa : pemeriksa bebas dari gangguan pribadi, ekstern, dan organisasi sehingga kualitas hasil pemeriksaan juga independen dan lebih baik.
Kualitas Hasil Pemeriksaan : indikatornya adalah Program Kerja Pemeriksaan (PKP), Temuan, Laporan Hasil Pemeriksaan (LHP), ekspose hasil pemeriksaan, dan tindak lanjut.
Berdasarkan uraian sebelumnya maka dapat digambarkan kerangka konseptual sebagai berikut.

Kualitas Hasil Pemeriksaan
(Y)
Kecakapan Profesional
(X3)
Pengalaman Bekerja
(X2)
Tingkat Pendidikan
(X1)

Independensi Pemeriksa
(X4)

3. Metode Penelitian
Jenis penelitian yang penulis lakukan adalah statistik deskriptif kausal yaitu desain penelitian yang meneliti suatu objek penelitian dengan tujuan untuk memberikan informasi mengenai karakteristik variabel penelitian yang utama dan data demografi responden jika ada. Desain ini berguna untuk menganalisis hubungan antara satu variabel dengan variabel lainnya atau bagaimana suatu variabel mempengaruhi variabel lainnya. Data yang igunakan penulis dalam menyusun adalah data primer dan data sekunder. Data Primer adalah data atau informasi yang berkaitan dengan penelitian ini dan diperoleh secara langsung tanpa melalui perantara dari sumber asli/ utama untuk menjawab pertanyaan penelitian, yang kemudian dikembangkan dengan pemahaman sendiri oleh penulis di dalam mengambil kesimpulan. Misalnya adalah kuesioner dan wawancara dengan pihak entitas yang berkaitan yaitu staf pada Badan Pengawasan Daerah Kabupaten Karo, data Sekunder adalah data yang sudah diolah dan telah menjadi dokumentasi yang bersumber dari entitas pemerintahan ataupun dari sumber-sumber lainnya, misalnya: sejarah singkat Pemerintah Daerah Kabupaten Karo, gambaran umum Badan Pengawasan Daerah Kabupaten Karo,
4. Analisis Hasil Penelitian
Badan Pengawasan Daerah Kabupaten Karo adalah lembaga yang dibentuk berdasarkan Peraturan Daerah Kabupaten Karo Nomor 19 Tahun 2008 yang merupakan Unsur Penunjang Pemerintah Daerah yang dipimpin oleh seorang Kepala yang berkedudukan di bawah dan bertanggung jawab langsung kepada Bupati melalui pembinaan Sekretaris Daerah. Badan Pengawasan Daerah mempunyai tugas untuk membantu Bupati di dalam pengawasan terhadap pelaksanaan urusan Pemerintahan di Daerah Kabupaten, pelaksanaan pembinaan atas penyelenggaraan Pemerintahan Desa dan pelaksanaan urusan Pemerintahan Desa. Badan Pengawasan Daerah terdiri dari bagian Tata Usaha, Bidang Pemerintahan, Bidang Pembangunan. Bidang Keuangan dan Kekayaan, dan Kelompok Jabatan Fungsional.
Sebelum melakukan pengujian data dan pengujian hipotesis, terlebih dahulu dilakukan pengujian atas kualitas data untuk menjamin bahwa data yang diperoleh sudah dapat digunakan dalam penarikan kesimpulan. Pengujian ini secara umum diarahkan untuk menguji alat ukur yang digunakan (kuesioner) serta data yang diperoleh dari responden. Kuesioner yang diajukan kepada responden berisikan 17 butir pertanyaan yang digunakan untuk mengukur 5 buah variabel penelitian.

4.1 Uji Asumsi Klasik
Metode analisi dara yang dipergunakan adalah metode analisis regresi berganda dengan bantuan software SPSS for windows. Penggunaan metode analisis regresi dalam pengujian hipotesis terlebih dahulu di uji apakah model tersebut memenuhi asumsi klasik atau tidak.
4.1.1 Uji Normalitas
Pengujian Normalitas dilakukan dengan menggunakan pengujian Kolmogorov-Smirnov. Pengujian dengan metode ini menyatakan jika nilai Kolmogorov-Smirnov memiliki probabilitas lebih besar dari 0,05 (Santoso, 2005), maka variabel penelitian tersebut dinyatakan berdistribusi normal. Bedasarkan hasil uji statistic dapat disimpulkan berdistribusi normal, nilai Asymp.sig (2-tailed) pada variable tingkat pendidikan adalah 0.424, variable pengalaman bekerja adalah 0,127, variable kecakapan professional adalah 0.88, independensi pemeriksa adalah 0.838, sedangkan variable kualitas hasil pemeriksaan memiliki nilai 0.128. Semua variable memiliki nilai > 0.05 sehingga berdistribusi normal.

4.1.2 Uji Multikolinearitas
Multikolinearitas dapat timbul jika variabel bebas saling berkorelasi satu sama lain, sehingga multikolinearitas hanya dapat terjadi pada regresi berganda. Hal ini mengakibatkan perubahan tanda koefisien regresi serta mengakibatkan fluktuasi yang besar pada hasil regresi. Deteksi dapatdilakukan dengan cara melihat nilai Variance Inflation factor dan toleransi. Seluruh variable independen memiliki nilai VIF F table maka dapat disimpulkan bahwa secara simultan (bersama-sama) antara Tingkat Pendidikan, Pengalaman Bekerja, Kecakapan Profesional dan Independensi Pemeriksa berpengaruh terhadap Kualitas Hasil Pemeriksaan, dari hasil uji t diperoleh persamaan regresi berganda sebagai berikut
Y = 5.466 – 0,516 X1 + 0,163 X2 + 0,215X3 + 0,705 X4 + e
Setelah uji t dilakukan maka dapat diketahui pengaruh dari masing masing variable independen terhadap variable dependen
a. Nilai t hitung variable tingkat pendidikan -1.415 dengan nilai signifikansi 0.169 sedangkan t table menunjukkan 2.05529 sehingga dapat disimpulkan bahwa t table > t hitung yang artinya Tingkat Pendidikan secara parsial tidak mempengaruhi signifikan terhadap Kualitas Hasil Pemeriksaan.
b. Nilai t hitung variable Pengalaman Bekerja 0,191 dengan nilai signifikansi 0.850 sedangkan t table menunjukkan 2.055529 sehingga dapat disimpulkan bahwa t table > t hitung yang artinya Pengalaman Bekerja secara parsial tidak mempengaruhi signifikan terhadap Kualitas Hasil Pemeriksaan.
c. Nilai t hitung variable Kecakapan Profesional 0.497 dengan nilai signifikansi 0,623 sedangkan t table menunjukkan 2.05529 sehingga dapat disimpulkan bahwa t table > t hitung yang artinya Kecakapan Profesional secara parsial tidak mempengaruhi signifikan terhadap Kualitas Hasil Pemeriksaan.
d. Nilai t hitung variable Independensi Pemeriksa 3.244 dengan nilai signifikansi 0.03 sedangkan t table menunjukkan 2.05529 sehingga dapat disimpulkan bahwa t hitung > t table yang artinya Independensi Pemeriksa secara parsial berpengaruh signifikan terhadap Kualitas Hasil Pemeriksaan.

4.2 Pembahasan Hasil Penelitian
Dari hasil pengujian secara individual (parsial), diketahui bahwa variabel Tingkat Pendidikan, Pengalaman Bekerja, Kecakapan Profesional tidak memiliki pengaruh yang signifikan terhadap Kualitas Hasil Pemeriksaan. Sedangkan variabel Independensi Pemeriksa memiliki pengaruh signifikan terhadap Kualitas Hasil Pemeriksaan (sign.0,007<0,05). Sedangkan nilai R Square 0,406 mengindikasikan bahwa Kualitas Hasil Pemeriksaan mampu dijelaskan oleh Tingkat Pendidikan, Pengalaman Bekerja, Kecakapan Profesional dan Independensi Pemeriksa sebesar 40,6% sedangkan selebihnya sebesar 59,4% dijelaskan oleh sebab-sebab yang lain. Secara parsial, hasil penelitian ini berbeda dengan hasil penelitian terdahulu yang dilakukan oleh Rizal Iskandar Batubara (2008) yang menyatakan bahwa secara parsial, variabel Kecakapan Profesional, Pendidikan Berkelanjutan, dan Independensi Pemeriksa berpengaruh secara signifikan terhadap Kualitas Hasil Pemeriksaan dengan tingkat kepercayaan 95% dan untuk Latar Belakang Pendidikan tidak berpengaruh secara parsial terhadap Kualitas Hasil Pemeriksaan. Tingkat Pendidikan (X1) memiliki hasil regresi yang menjelaskan bahwa variabel independen Tingkat Pendidikan mempunyai pengaruh negatif terhadap Kualitas Hasil Pemeriksaan, artinya setiap kenaikan Tingkat Pendidikan tidak mempengaruhi secara signifikan Kualitas Hasil Pemeriksaan. Pengalaman Bekerja (X2) memiliki hasil regresi yang menjelaskan bahwa variabel independen Pengalaman Bekerja mempunyai pengaruh positif terhadap Kualitas Hasil Pemeriksaan, akan tetapi tidak berpengaruh secara signifikan terhadap Kualitas Hasil Pemeriksaan karena nilai t hitung < t tabel (0.191 < 2,055529) yang artinya H0 diterima Kecakapan Profesional (X3) memiliki hasil regresi yang menjelaskan bahwa variabel independen Kecakapan Profesional mempunyai pengaruh positif terhadap Kualitas Hasil Pemeriksaan, akan tetapi tidak berpengaruh secara signifikan terhadap Kualitas Hasil Pemeriksaan karena nilai t hitung < t tabel (0.497 < 2,055529) yang artinya H0 diterima. Independensi Pemeriksa (X4) memiliki hasil regresi yang menjelaskan bahwa variabel independen Independensi Pemeriksa mempunyai pengaruh positif terhadap Kualitas Hasil Pemeriksaan, artinya setiap kenaikan Tingkat Pendidikan turut meningkatkan Kualitas Hasil Pemeriksaan.

5. Kesimpulan dan Saran
5.1 Kesimpulan
Berdasarkan penelitian yang dilakukan maka dapat disimpulkan bahwa :
1. Hasil pengujian secara bersama-sama (simultan) menunjukkan bahwa variabel Tingkat Pendidikan (X1), Pengalaman Bekerja (X2), Kecakapan Profesional (X3) dan Independensi Pemeriksa (X4) berpengaruh positif dan signifikan terhadap Kualitas Hasil Pemeriksaan (Y) pada Badan Pengawasan Daerah Kabupaten Karo. Hal ini menunjukkan bahwa Kualitas Hasil Pemeriksaan pada Badan Pengawasan Daerah Kabupaten Karo dipengaruhi oleh Tingkat Pendidikan, Pengalaman Bekerja, Kecakapan Profesional dan Independensi Pemeriksa.
2. Hasil pengujian secara satu persatu (parsial) menunjukkan bahwa variabel Kecakapan Profesional (X3) secara signifikan mempengaruhi Kualitas Hasil Pemeriksaan. Sedangkan variabel Tingkat Pendidikan (X1), Pengalaman Bekerja (X2) dan Independensi Pemeriksa (X4) tidak berpengaruh secara signifikan terhadap Kualitas Hasil Pemeriksaan.
3. Variabel Kecakapan Profesioanal (X3) merupakan variabel yang paling berpengaruh secara dominan dalam Kualitas Hasil Pemeriksaan pada Badan Pengawasan Daerah Kabupaten Karo.

5.2 Saran

Adapun saran yang diberikan penulis bagi Badan Pengawas Daerah Kabupaten Karo adalah:
1. Badan Pengawasan Daerah Kabupaten Karo beserta instansi terkait perlu memberikan pelatihan (training) yang berhubungan secara langsung dengan pemeriksaan termasuk teknologi-teknologi pemeriksaan terbaru untuk meningkatkan kualitas pemeriksaan. Badan Pengawasan Daerah Kabupaten Karo juga perlu menyelenggarakan pendidikan berkelanjutan bagi staf Badan Pengawasan Daerah Kabupaten Karo untuk mengatasi Sumber Daya Manusia (SDM) yang kurang memadai.
2. Bagi peneliti selanjutnya, perlu untuk memperbanyak item untuk menilai variabel agar diperoleh gambaran yang lebih optimal dan menambah sampel seperti Bawasda atau Bawasko dari kabupaten atau kota lain.
3. Kategori responden yang digunakan juga sebaiknya ditambah, bukan hanya pemeriksa atau auditor, tetapi juga yang diperiksa (auditee) sehingga pengambilan kesimpulan dapat dilakukan dengan lebih baik.
4. Variabel lain yang kemungkinan memberikan pengaruh pada Kualitas Hasil Pemeriksaan sebaiknya ditambahkan ke dalam model penelitian, seperti: loyalitas, kecukupan waktu, Program Kerja Pemeriksaan (PKP), dan lain-lain.

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