Improving the asymmetric stochastic volatility model with ex-post volatility: the identification of the asymmetry
模拟研究发现非对称随机波动模型因预设的收益-波动关系可能推断出错误的相关性。本文通过引入事后波动率来识别相关性参数，在美国主要股指中估计出的收益与波动过程相关性显著减小，且联合估计收益与事后波动过程能提升样本外指数收益分布预测表现。
Simulation studies show that the asymmetry stochastic volatility (ASV) models may infer erroneous correlation coefficients, due to their predetermined return-volatility specification. We propose identifying the correlation parameter by incorporating the ex-post volatility in the ASV framework. We obtain a significantly smaller magnitude in the estimated correlation coefficients between equity and volatility processes among major U.S. equity market indexes. Out-of-sample index return distribution forecasts demonstrate superior performance when jointly estimating the return and the ex-post volatility processes. The corrected return-volatility correlations by estimating proposed ASV models with subsample data further document the time-varying leverage effect.