基于实现极差和实现波动率的中国金融市场风险测度研究

The Study on Financial Market Risk Measures in China Based on Realized Range and Realized Volatility

目前比较流行的金融市场风险价值研究一般采用日收益数据,并基于GARCH类模型进行估计和预测。本文利用沪深股指日内高频数据,分别通过ARFIMA模型和CARR模型对实现波动率和较新的实现极差建模,计算风险价值。通过对VaR的似然比和动态分位数等回测检验,实证分析了各种模型的VaR预测能力。结果显示,使用日内高频数据的实现波动率和实现极差模型的预测能力强于采用日数据的各种GARCH类模型。

Current studies on financial market risk measures usually use daily returns based on GARCH type models. By using intraday high frequency data of Shanghai and Shenzhen stock indices, the paper builds up a realized volatility model and a realized range model based on ARFIMA model and CARR model respectively, which are applied to calculate VaR. The authors also employ the Kupiec LR test and dynamical quantile test to compare the performance of VaR forecasting of all models. Empirical results show that realized volatility and realized range models based on intraday data are better than GARCH type models based on daily returns.