方差风险溢价在波动率预测中的应用——基于上证50ETF期权

Application of Variance Risk Premium in Volatility Forecast:Based on SSE 50ETF Options

波动率在金融衍生品定价等领域具有广泛的应用,因此涌现了大量的学者针对波动率预测展开研究。目前流行的布莱克-斯科尔斯(Black-Scholes)模型隐含波动率以及无模型隐含波动率,均是在风险中性测度下对未来已实现波动率的预测,而无模型隐含波动率更是基于实际市场数据所得,二者之间的差别即方差风险溢价会在一定程度上导致波动率预测的偏差。基于此,在构建传统的波动率预测指标后,再通过方差风险溢价对隐含波动率进行修正,分别通过单变量回归和多变量回归对不同的波动率预测指标的预测能力以及包含信息量进行分析,结果发现方差风险溢价可以有效降低预测偏差,增强对已实现波动率的预测能力。

Volatility is widely applied in financial derivatives pricing and other fields,and a large number of scholars have made studies on volatility forecasting.However,the popular Black-Scholes model implied volatility and model-free implied volatility are both forecasts of the realized volatility under the risk-neutral measure,while the latter is derived from physical measure.The difference between the two,namely the variance risk premium,will lead to bias in volatility forecasting.Based on this,after constructing the traditional volatility forecast indicators,the paper takes the variance risk premium to adjust the implied volatility.Through univariate OLS regressions and Encompassing regressions,it analyzes the forecasting ability of different volatility and the information content.It turns out that the variance risk premium can effectively reduce the forecast bias and enhance the ability to forecast the realized volatility.