Realized GAS-GARCH及其在VaR预测中的应用

Realized GAS-GARCH model and its application in Value-at-Risk forecast

论文提出了新的波动率模型Realized GAS-GARCH,并推导了该模型的QMLE参数估计.该模型结合了Generalized Autoregressive Score(GAS)模型的基本思路,把Realized GARCH模型扩展到包含厚尾分布的情形,并采用了与厚尾分布参数相依的冲击响应函数.与简单的厚尾分布扩展模型相比,这种设定对于回报率中的极端值更加稳健.在基于沪深300指数高频数据的实证结果中,使用GAS冲击响应函数的模型对"在险价值"VaR的预测能力显著的超过了传统的厚尾Realized GARCH模型.

This paper proposed a new volatility model-Realized GAS-GARCH, and derived its Quasi-MLE es- timator for the model parameters. In the light of Generalized Autoregressive Score （GAS） model[3] , this paper extended Realized GARCH model[6] to fat-tail distribution with an appropriated distribution dependent impulse response function. Compared with the simple distribution modification, the current model is more robust to ex- treme returns. Empirical results from HuShen 300 high frequency data show that the Realized GARCH model with GAS impulse response function outperforms traditional Realized GARCH structure with fat-tail distribu- tions.