Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models
比较了ARFIMA、HAR和EGARCH三种模型在预测股票特质波动率上的表现，发现HAR模型预测效果最好，且不同模型下特质波动率与股票收益的关系有正有负。
Abstract We investigate the performances of the ARFIMA, HAR, and EGARCH models in capturing the time-varying property of idiosyncratic volatility (IVOL). We find that the expected IVOL predictions by HAR are superior. In diverse portfolio scenarios, a greater degree of judgment is required to assess the pricing ability of expected IVOLs. For the lowest value-weighted quintiles and the expected IVOL estimated by the HAR model, the IVOL-return relationship is negative. Conversely, the IVOL-return relationship is positive for the expected IVOL estimated by the EGARCH model. Further evidence suggests a complicated and mixed relationship between the expected IVOL estimated by the ARFIMA model and stock returns.