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### 基于混频已实现GARCH模型的波动预测与VaR度量

• 出版日期:2018-01-25 发布日期:2018-01-25

### Mix Frequency Realized GARCH Models: The Forecast of Volatility and Measure of VaR

Yu Xiaojian & Wang Xiuhua

• Online:2018-01-25 Published:2018-01-25

Abstract: This paper extends Hansen et al. (2012)’s Realized GARCH model to a mixed frequency Realized GARCH model (M-Realized GARCH model), including the data of intraday return, daily return and realized volatility. In our new model, we divide each trading day into two periods, and add mix frequency mean equations. We both build the conditional volatility equation of the residuals in mean equations and realized volatility equation. Mix frequency data of CSI 300 index's during 2013-2016 are analyzed in this paper. Different distributions including normal distribution, t distribution and generalized error distribution are discussed too. The results of Loss function, SPA Test, kupiec Test and Dynamic Quantile Test show that the volatility predict power measure of VaR of M-Realized GARCH model are better than the GARCH, Realized GARCH. Moreover, actual failure rate of VaR is consistent with the theoretical failure rate, and there is no correlation between the two failures occurred. In the end, we use Block bootstrap method to sample mix frequency data, and further prove that the M-Realized GARCH model has higher prediction accuracy than the Realized GARCH model.