VIX futures pricing based on high‐frequency VIX: A hybrid approach combining SVR with parametric models
提出一种结合支持向量回归与参数模型的混合方法，利用高频VIX计算的已实现半方差来改进VIX期货定价，实证表明该方法显著提升了定价准确性。
Abstract We propose a novel hybrid approach for volatility index (VIX) futures pricing by combining support vector regression (SVR) with parametric models. Realized semivariances calculated based on high‐frequency VIX are used to characterize the asymmetric shocks of VIX, and the direct pricing framework of the heterogeneous autoregressive model is extended by incorporating realized semivariances. VIX futures prices are first obtained via parametric models, then the predicted prices and realized semivariances are input into SVR to obtain the final predicted values. Empirical results indicate that the combination of SVR with parametric models significantly improves the pricing ability. This indicates the important information of high‐frequency VIX and the necessity of combining machine learning methods with parametric models to obtain more accurate predictions.