GARCH类模型波动率预测评价

GARCH类模型已经广泛运用于波动率的预测,但对模型的预测表现进行评价却受到了忽视,其主要原因是缺乏合适的衡量标准。本文首先运用GARCH类模型对上证指数收益率进行了全面的估计及样本外预测,然后以已实现波动率作为波动率预测的评价标准,通过M-Z回归和损失函数来评价GARCH类模型的波动率预测表现。结果表明,无论是样本内还是样本外,GARCH类模型都能够较好的预测上证指数的收益波动率。其中,偏斜t-分布假设下的GJR(1,1)模型的预测能力最强。

Evaluation on Volatility Forecasting Performance of GARCH-Type Models

GARCH-type models have been broadly used to forecast volatility.But it's ignored to evaluate the performance of volatility forecasting. The reason is mainly lack of appropriate benchmark to evaluate. We estimate and forecast the return of SZZS using GARCH-type models. Realized volatiliky is computed as benchmark using 5-minuets high frequency data. Volatility forecasting performance is measured using M-Z regression and loss function. The conclusion is that GARCH type models have a very goodforecasting performance both in sample and out of sample, and GJR(1,1) under skewed t-distribution assumption is the most powerful to forecast.