[17:17 2/2/2015 RFS-hhu078.tex] Page: 876 876–912 Self-Exciting Jumps, Learning, and Asset Pricing Implications

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. (JEL C11, C13, C32, G12) The financial meltdown of 2008 and the European debt crisis of 2010 to 2012 have impacted financial markets worldwide and have had far-reaching consequences for the world economy. These market turmoils raise questions about how likely extreme events are and how they can be modeled. Recent empirical studies find that a big jump in asset prices tends to be associated with an abrupt move in asset volatility, a phenomenon labeled as co-jumps o