GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets |
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| Institution: | 1. Department of Risk Management and Insurance, Tamkang University, 151, Yingzhuan Rd., Tamsui Dist., New Taipei City 25137, Taiwan;2. Department of Risk Management and Insurance, Risk and Insurance Research Center, College of Commerce, National Chengchi University, 64, Sec. 2, Zhi-Nan Road, Wen-Shan District, Taipei 11605, Taiwan;1. School of Finance, Nanjing Agricultural University, Weigang 1#, Nanjing 210095, PR China;2. School of Economics and Management, Southeast University, Sipailou 2#, Nanjing 210096, PR China;1. School of Statistics, Xi’an University of Finance and Economics, Xi’an 710100, China;2. School of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom;1. School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China;2. School of Business, Jiangsu Normal University, Xuzhou 221116, China |
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| Abstract: | This study proposes a generalized autoregressive conditional heteroskedasticity (GARCH)-mixed data sampling (MIDAS)-generalized autoregressive score (GAS)-copula model to calculate conditional value at risk (CoVaR). Our approach leverages the GARCH-MIDAS model to enhance stock market volatility modeling and incorporates the GAS mechanism to create a copula with dynamic parameters. This approach allows for the precise calculation of both CoVaR and its changes over time (delta CoVaR). The results of our study demonstrate a significant improvement in CoVaR calculation accuracy compared to other models, showcasing the effectiveness of the GARCH-MIDAS-GAS-copula model. In addition, the CoVaR indicator provides a more comprehensive view of risk spillover relationships compared to value at risk (VaR), offering deeper insights into the asymmetrical risk transmission dynamics between the Chinese and US stock markets, providing valuable information for risk management and investment decisions. |
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| Keywords: | GARCH-MIDAS Copula GAS Financial risk CoVaR |
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