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.

Published in Review of Financial Studies https://doi.org/10.1093/rfs/hhu078

This paper examines the effect of a firm's crash-risk exposure on its speed of leverage adjustment (SOA), and how this effect is influenced by the information environment of the country in which the firm is located. We employ a panel of 19,247 firms across 41 countries from 1989 to 2013, and we find that firms with a higher crash-risk exposure tend to adjust their financial leverages more slowly toward their targets. This evidence supports the dynamic trade-off theory that firms with larger transaction costs adjust their capital structures less often. Equally important, we document that the negative link between crash-risk exposure and SOA is less pronounced in countries with a more transparent information environment.