GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets

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.