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.

中文翻译：

---

好的和坏的波动性：一类新的不对称异方差模型

本文介绍了一类新的易处理的不对称异方差模型，即好波动率和坏波动率（GBV）。GBV分量的动力学中分别识别出不对称性，分别对应于正冲击和负冲击。GBV模型允许两个条件半方差根据具有不同半确定分布的两个独立函数形式演化。对六种主要指数收益的经验应用表明，与金融文献中众所周知的非对称波动率模型相比，拟合度得到了改善。该模型进一步提高了预测效果。得出的非平凡的新闻影响曲线传达了一个二分法，即财务收益的不对称性对正面和负面冲击具有不同的动力。