52 Pages Posted: 7 Jan 2012 Last revised: 18 Jun 2014
Date Written: June 18, 2014
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. It is found that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting and option pricing.
Keywords: Self-Excitation, Jump Clustering, Tail Behaviors, Parameter Learning, Sequential Bayes Factor, Excess Volatility, Volatility Forecasting, Option Pricing
JEL Classification: C11, C13, C32, G12
Suggested Citation: Suggested Citation
Fulop, Andras and Li, Junye and Yu, Jun, Self-Exciting Jumps, Learning, and Asset Pricing Implications (June 18, 2014). Available at SSRN: https://ssrn.com/abstract=1981024 or http://dx.doi.org/10.2139/ssrn.1981024