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Research Scholar, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
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Associate Professor, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
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Research Scholar, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
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Associate Professor, Department of Management Studies, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India
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Associate Professor, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
Associate Professor, Department of Information Technology, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India
Volatility is one of the most important factors in investment decision-making and financial risk management, and the role of volatility modelling in comprehending market dynamics is crucial. In this study, the volatility behaviour of six major stock markets in Asia—India, China, Japan, South Korea, Singapore and Hong Kong—has been explored with the GARCH (1,1) model. The daily closing price data for six stock indices, the S&P 500, gold, and the price of Brent crude oil from January 2010 to December 2025 (4,166 observations per series) were analysed. The descriptive statistics, Pearson correlation analysis, Augmented Dickey–Fuller (ADF) unit root test, ARCH-LM test, and GARCH (1,1) estimation were used. The ADF test results indicated that all the series were stationary (ADF statistics varied from −9.95 to −66.83 at a p-value < 0.001). The ARCH-LM test results showed that there was significant conditional heteroskedasticity across all the markets (the LR test statistic varied from 155.07 to 1275.50 at a p-value < 0.001). The estimated GARCH models showed that the volatility shocks were very persistent, as the ARCH and GARCH effects were found significant, with the values of the volatility persistence (α + β) in the range of 0.9451 to 0.9882. A moderate degree of integration was observed across Asian markets, with the highest correlation between Singapore and Hong Kong (r = 0.5751), while the equity returns had a weak correlation with gold. The satisfactory forecasting performance of the models was indicated by the results from the model evaluation, where the prediction errors (RMSE and MAE) for Singapore were the lowest. The results indicate that the GARCH (1,1) model is an appropriate model for volatility modelling and can offer useful information to investors, portfolio managers, financial analysts, and policymakers.
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