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Journals / Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2025 / Cilt: 16 Sayı: 32

INVESTIGATING THE INTERDEPENDENCE OF STOCK EXCHANGES USING MULTIVARIATE STOCHASTIC VOLATILITY MODELS

ÇOK DEĞİŞKENLİ STOKASTİK VOLATİLİTE MODELLERİNİ KULLANARAK BORSALARIN KARŞILIKLI BAĞIMLILIĞININ ARAŞTIRILMASI

Gönül Yüce Akıncı(ORDU UNIVERSITY)0000-0002-5900-7114
Merter Akıncı(ORDU UNIVERSITY)0000-0002-5449-0207
Pages
691–730
Publication Date
2025-12-30
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

The findings reveal the presence of significant volatility clustering and persistence, particularly in Asian markets, as well as substantial intra- and inter-regional spillover effects.

📌 3 Key Takeaways

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Objective & Hypothesis:This study investigates the interdependence of major global stock exchanges by examining volatility clustering, uncertainty, spillover dynamics, and time-varying return correlations using multivariate stochastic volatility (MSV) models.
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Key Findings & Data:Drawing on daily data from nine major indices representing the United States, Europe, and Asia—specifically, the S&P 500, Dow Jones, NASDAQ, DAX, CAC 40, FTSE 100, Nikkei 225, Hang Seng, and Shanghai Composite—the analysis spans from January 5, 2001, to January 5, 2024, offering a comprehensive view of long-term market interactions.
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Conclusion & Impact:The findings reveal the presence of significant volatility clustering and persistence, particularly in Asian markets, as well as substantial intra- and inter-regional spillover effects.
🔬Design:Modeling & Simulation
📍Scope:2001 Verileri
⚙️Variables:Stokastik volatilite modelleri · borsalar · Bayesyen yaklaşım

Abstract

This study investigates the interdependence of major global stock exchanges by examining volatility clustering, uncertainty, spillover dynamics, and time-varying return correlations using multivariate stochastic volatility (MSV) models. Drawing on daily data from nine major indices representing the United States, Europe, and Asia—specifically, the S&P 500, Dow Jones, NASDAQ, DAX, CAC 40, FTSE 100, Nikkei 225, Hang Seng, and Shanghai Composite—the analysis spans from January 5, 2001, to January 5, 2024, offering a comprehensive view of long-term market interactions. The empirical approach is based on three model variations: the standard MSV, Constant Correlation MSV (CC-MSV), and Dynamic Correlation MSV (DC-MSV), estimated within a Bayesian framework using Markov Chain Monte Carlo (MCMC) methods. The findings reveal the presence of significant volatility clustering and persistence, particularly in Asian markets, as well as substantial intra- and inter-regional spillover effects. Volatility shocks originating from one market were shown to influence others, with U.S. markets identified as the primary transmitters of global volatility. Additionally, the results indicate that the correlations between markets are dynamic and tend to intensify during periods of financial distress, reflecting stronger market co-movements in times of global uncertainty. The DC-MSV model, which allows for both time-varying volatility and correlations, outperforms the other specifications, as evidenced by lower Deviance Information Criterion (DIC) values across regions. These results underscore the increasing integration of global financial markets and the diminishing effectiveness of traditional geographic diversification strategies. From a policy perspective, the findings emphasize the need for financial regulators to monitor international volatility spillovers and systemic risks, particularly in an era of heightened financial globalization. For investors and portfolio managers, understanding the structure and dynamics of cross-market volatility is crucial for improving risk management, portfolio allocation, and hedging strategies. This study makes a significant contribution to the literature by applying advanced stochastic volatility models to a large and geographically diverse set of stock markets, providing robust insights into the evolving nature of global market interdependence.

Özet

Bu çalışma, çok değişkenli stokastik volatilite (MSV) modellerini kullanarak büyük küresel borsaların birbirine bağımlılığını, volatilite kümelemesi, belirsizlik, yayılma dinamikleri ve zamanla değişen getiri korelasyonları açısından incelemektedir. Amerika Birleşik Devletleri, Avrupa ve Asya'yı temsil eden dokuz büyük borsanın (S&P 500, Dow Jones, NASDAQ, DAX, CAC 40, FTSE 100, Nikkei 225, Hang Seng ve Şanghay Bileşik Endeksi) 5 Ocak 2001 ile 5 Ocak 2024 arasındaki günlük verilerini kapsayan çalışma, uzun vadeli piyasa etkileşimlerine kapsamlı bir bakış sunmaktadır. Ekonometrik yaklaşım, standart MSV, Sabit Korelasyonlu MSV (CC-MSV) ve Dinamik Korelasyonlu MSV (DC-MSV) olmak üzere üç model varyasyonuna dayanmış ve bu modeller Markov Zincir Monte Carlo (MCMC) yöntemleri kullanılarak Bayesyen bir çerçevede tahmin edilmiştir. Bulgular, özellikle Asya pazarlarında belirgin volatilite kümenlemesi ve sürekliliğinin yanı sıra bölge içi ve bölgeler arası yayılma etkilerini ortaya koymuştur. Bir piyasadan kaynaklanan volatilite şoklarının diğer piyasaları etkilediği gözlemlenmiş ve ABD piyasalarının küresel volatilitenin başlıca kaynağı olduğu tespit edilmiştir. Ayrıca, piyasalar arasındaki korelasyonların dinamik olduğu ve finansal kriz dönemlerinde yoğunlaşarak küresel belirsizlik zamanlarında daha güçlü piyasa eş hareketliliği yansıttığı sonucuna varılmıştır. Hem zamanla değişen volatilite hem de korelasyonları modelleyen DC-MSV modeli, daha düşük Deviance Information Criterion (DIC) değerleriyle diğer modellerden daha iyi performans göstermiştir. Bu sonuçlar, küresel finansal piyasaların giderek daha fazla entegre olduğunu ve geleneksel coğrafi çeşitlendirme stratejilerinin etkinliğinin azaldığını vurgulamaktadır. Politika perspektifi bakımından bulgular, finansal düzenleyicilerin uluslararası volatilite yayılımlarını ve sistemik riskleri özellikle finansal küreselleşmenin arttığı dönemlerde izlemesi gerektiğini göstermektedir. Yatırımcılar ve portföy yöneticileri için bulgular piyasalar arası volatilite yapısını ve dinamiklerini anlamak, risk yönetimi, portföy tahsisi ve korunma stratejilerini anlamak bağlamında kritik öneme sahiptir. Bu çalışma, gelişmiş stokastik volatilite modellerini geniş ve coğrafi olarak çeşitli borsalar grubuna uygulayarak küresel piyasa bağımlılığının değişen doğasına ilişkin sağlam içgörüler sunmakta ve literatüre önemli bir katkı sunmayı amaçlamaktadır.

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Cite This Article

Akıncı, G. Y., Akıncı, M. (2025). INVESTIGATING THE INTERDEPENDENCE OF STOCK EXCHANGES USING MULTIVARIATE STOCHASTIC VOLATILITY MODELS. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 16 Sayı: 32, 691–730. https://doi.org/10.36543/kauiibfd.2025.026

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