News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns
构建了一个由潜在新闻过程驱动的收益分布成分模型，将条件方差分解为跳跃和平滑变化部分，并允许跳跃和正常冲击非对称地反馈到预期波动率，从而改进波动率预测，尤其适用于大幅股价变动后的情景。
ABSTRACT This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time‐varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically into expected volatility. This model improves forecasts of volatility, particularly after large changes in stock returns. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, leverage effects, and the time‐series dynamics of jump clustering.