股指期货波动率的半参数预测模型及其MCS检验

A Semiparametric Forecasting Model for Volatility of Stock Index Futures and Its MCS Test

股指期货在资本市场价格发现和风险防范过程中扮演重要角色,科学准确的预测其收益波动率对充分实现股指期货的市场功能具有重要的理论和现实价值。将线性非负模型扩展为半参数的预测模型,用来预测股指期货市场的已实现波动率,并探讨了该模型估计方法的渐进性质。此外,以沪深300股指期货的5 min高频交易数据为例,运用滚动时间窗的样本外预测和最新发展起来的具有Bootstrap特性的MCS检验,在多种稳健损失函数下,实证评价和比较新构建的半参数预测模型与其他7类波动率预测模型对沪深300股指期货已实现波动率的预测能力。实证结果表明,在多种稳健损失函数的评价标准下,新构建的半参数预测模型是预测性能最好的模型。

Stock index futures plays an important role in the process of price discovery and risk preven- tion of capital market. The prediction of its return volatility is significantly important to achieve the risk a- version function of stock index futures. A semiparametric forecasting model based on the linear nonnega- tive autoregressive model is proposed to forecast the realized volatility of stock index futures, and the as- ymptotic properties of estimation method for this model are analyzed. In addition, taking 5 min high-fre- quency trading data of CSI300 index futures as example, the out-of-sample daily volatility predictions cal- culated by using rolling predicting method, and a bootstrap MCS test is used to evaluate the predicting ac- curacy for the proposed model and other 7 models. The empirical results show that, under various robust loss functions, the proposed model is the best model for volatility predictions of stock index futures among the 8 models.