Volatility and Dynamic Correlation of Global Stock Markets: Evidence from Switzerland, Japan, Turkey, and Indonesia (2020–2025)
Keywords:
volatility, dynamic correlation, GARCH, DCC-GARCH, global stock markets, financial crisisAbstract
This study examines the volatility and dynamic correlations of stock market indices in Switzerland, Japan, Turkey, and Indonesia during the period 2020–2025, which was characterized by heightened global uncertainty. This period was characterized by heightened uncertainty due to the COVID-19 pandemic, monetary policy tightening, and geopolitical tensions that affected global financial market stability. The study utilizes secondary data consisting of daily closing prices of the Swiss Market Index (SMI), Nikkei 225, BIST 100, and the Indonesia Composite Index (IHSG), which were transformed into logarithmic returns. After synchronizing trading dates, the final dataset comprised 1,310 observations for each index. The analysis was conducted using Python and employed descriptive statistics, unit root tests, ARCH-LM tests, GARCH models, and DCC-GARCH models. The results indicate that all stock markets exhibit volatility clustering and persistent volatility, with the BIST 100 showing the highest volatility and the Nikkei 225 exhibiting the lowest volatility persistence and faster adjustment to market shocks. Furthermore, the DCC-GARCH model reveals time-varying correlations with high persistence, indicating that changes in inter-market relationships remain influential over time. These findings suggest that international stock markets are interconnected and that dynamic correlations tend to strengthen and remain influential during periods of heightened uncertainty, potentially reducing the benefits of international diversification.
References
Ampountolas, A. (2023). The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis. Journal of Risk and Financial Management, 16(1), 25. https://doi.org/10.3390/jrfm16010025
Balcı Yıldız, N. (2024). Volatility Spillover Effects Between Stock Markets During the Crisis Periods: Diagonal BEKK Approach. Pamukkale University Journal of Social Sciences Institute, (65), 229–246. https://doi.org/10.30794/pausbed.1462608
Bank for International Settlements. (2024). Annual economic report June 2024. https://www.bis.org/annualeconomicreports/index.htm
Bodie, Zvi., Kane, Alex., & Marcus, A. J. . (2021). Investments (12th ed.). McGraw-Hill Education.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Bouri, E., Demirer, R., Gabauer, D., & Gupta, R. (2022). Financial Market Connectedness: The Role of Investors’ Happiness. Finance Research Letters, 44, 102075. https://doi.org/10.1016/j.frl.2021.102075
Brooks, C. (2019). Introductory Econometrics for Finance (4th ed.). Cambridge University Press. https://doi.org/10.1017/9781108524872
Cboe Global Indices. (2026). Cboe Volatility Index Mathematics Methodology. https://cdn.cboe.com/resources/indices/Cboe_Volatility_Index_Mathematics_Methodology.pdf
Cheema, M. A., Faff, R., & Szulczyk, K. R. (2022). The 2008 Global Financial Crisis and COVID-19 Pandemic: How Safe Are the Safe Haven Assets? International Review of Financial Analysis, 83, 102316. https://doi.org/10.1016/j.irfa.2022.102316
Creswell, J. W. ., & Creswell, J. David. (2023). Research design : qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications.
Drake, P. P., Fabozzi, F. J., & Fabozzi, F. A. (2022). Financial Management and Investment Management. World Scientific Publishing. https://doi.org/10.1142/12352
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
Engle, R. F. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20(3), 339–350.
Feder-Sempach, E., Szczepocki, P., & Bogołębska, J. (2024). Global Uncertainty and Potential Shelters: Gold, Bitcoin, and Currencies As Weak and Strong Safe Havens for Main World Stock Markets. Financial Innovation, 10(1), 67. https://doi.org/10.1186/s40854-023-00589-w
Gençyürek, A. (2024). Volatility Modeling and Spillover: The Turkish and Russian Stock Markets. Istanbul Business Research, 53(1), 81–101. https://doi.org/10.26650/ibr.2024.53.162811
Gujarati, D. N. (2023). Essentials of Econometrics. SAGE.
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice. OTexts.
International Monetary Fund. (2023). Global Financial Stability Report, Financial and Climate Policies for A High-Interest-Rate Era, October 2023. https://www.imf.org/-/media/files/publications/gfsr/2023/october/english/text.pdf
International Monetary Fund. (2025). World Economic Outlook, October 2025: Global Economy in Flux, Prospects Remain Dim. International Monetary Fund. https://doi.org/10.5089/9798229023948.081
Perktold, J., Seabold, S., & Taylor, J. (2024). Statsmodels: Statistical modeling in Python. Zenodo. https://doi.org/https://doi.org/10.5281/ZENODO.593847
Sahabuddin, M., Islam, Md. A., Tabash, M. I., Alam, Md. K., Daniel, L. N., & Mostafa, I. I. (2023). Dynamic Conditional Correlation and Volatility Spillover between Conventional and Islamic Stock Markets: Evidence from Developed and Emerging Countries. Journal of Risk and Financial Management, 16(2), 111. https://doi.org/10.3390/jrfm16020111
Sun, X., Liu, J., Zhang, J., & Wang, C. (2023). The Dynamic Correlation of Stock Markets in the World’s Five Largest Economies—Based on DCC-GARCH Model. International Journal of Economics and Finance, 15(3), 27–34. https://doi.org/10.5539/ijef.v15n3p27
Tronzano, M. (2023). Safe-Haven Currencies as Defensive Assets in Global Stocks Portfolios: A Reassessment of the Empirical Evidence (1999–2022). Journal of Risk and Financial Management, 16(5), 273. https://doi.org/10.3390/jrfm16050273
World Bank. (2022). World Development Report 2022: Finance for An Equitable Recovery. https://www.worldbank.org/en/publication/wdr2022
Wu, S., Liang, Y., & Li, W. (2024). Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic. Journal of Applied Mathematics and Physics, 12(09), 3066–3080. https://doi.org/10.4236/jamp.2024.129184
