Self-Exciting Jumps, Learning, and Asset Pricing Implications
提出一个自激资产定价模型，考虑价格与波动率的共同跳跃及跳跃聚类，用贝叶斯学习进行实时分析，发现自激跳跃聚类在1987年股灾后明显，2008年金融危机时更突出，且学习影响收益分布的尾部行为，对风险管理、波动率预测和期权定价有重要意义。
The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real-time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. We also find that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting, and option pricing.