Modeling and forecasting stock return volatility using the HARGARCH model with VIX information
提出一种结合波动率指数信息与熵倾斜技术的新方法，通过利用风险中性分布的一阶和二阶矩改进传统模型，显著提升股票收益波动率预测精度，对衍生品市场风险管理有重要价值。
Abstract This study develops a novel approach for improving stock return volatility forecasts using volatility index information with the entropic tilting technique. Unlike traditional linear heteroskedasticity autoregressive methods with option‐implied information, we first derive predictive densities from traditional models, and then tilt using both the first and second moments of the risk‐neutral distribution, which enables us to capture the nonlinear effect in our specification. The empirical findings demonstrate a substantial enhancement in the forecasting accuracy of all models once the first‐ and second‐moment information is considered, where the improvement is both statistically and economically significant. These results have important implications for risk management in well‐established derivatives markets.