The Good and Bad Volatility: A New Class of Asymmetric Heteroskedastic Models
提出一类新的可处理非对称异方差模型（GBV），将波动分解为对应正负冲击的好与坏成分，实证表明该模型在拟合和预测六种主要指数收益率方面优于现有非对称波动模型。
Abstract This paper introduces a new class of tractable asymmetric heteroskedastic models, the good and bad volatility (GBV). Asymmetry is recognized in the dynamics of GBV components that correspond to positive and negative shocks respectively. The GBV model allows both conditional semivariances to evolve according to two separate functional forms with different semi‐definite distributions. An empirical application to six major index returns shows a fitting improvement over well‐known asymmetric volatility models in the financial literature. The model further leads to significant improvements in forecasting performance. The derived nontrivial news impact curves convey the dichotomy that asymmetry in financial returns has different dynamics for positive and negative shocks.