Domain Stabilization for Model-Free Option Implied Moment Estimation
提出域稳定方法改进无模型期权隐含矩估计，用标普500期权数据验证其能提升预测和预测能力，尤其优于其他处理方法。
Abstract We propose a new method, domain stabilization (DStab), to enhance the return predictive and forecasting ability of model-free option-implied moment estimators. Analyzing S&P 500 options data from January 2015 to December 2021, we show that DStab improves moment estimation consistency by stabilizing the integration domain, leading to better predictive and forecasting performance. When the options data characteristics are appropriately considered, DStab enhances both in-sample predictive and out-of-sample forecasting abilities of implied moments. DStab’s out-of-sample forecasting ability surpasses other treatment methods.