Portfolio Value-at-Risk estimating on markov regime switching copula-autoregressive conditional jump intensity-threshold generalized autoregressive conditional heteroscedasticity model

Considering asymmetric dependence between assets and jump dynamics and asymmetric volatility in asset returns, in this paper, we develop a markov regime switching copula-autoregressive conditional jump intensity-threshold generalized autoregressive conditional heteroscedasticity model in order to estimate the Value-a-Risk of industry indices portfolios. The empirical research shows that, the markov regime switching copula-autoregressive conditional jump intensity-threshold generalized autoregressive conditional heteroscedasticity model can comprehensively reflect the probability of extreme returns, which makes it optimal in estimating Value-at-Risk and outperforming other models which ignore jump dynamics in assets returns or asymmetric dependence between assets or asymmetric volatility in asset returns. Those above suggest that the model we construct can improve the accuracy of VaR estimating, and help investors make cautious decisions to manage risk effectively.