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.

We study the quantitative impact of lender control rights on corporate investment, asset prices, and the aggregate economy. We build a general equilibrium model in which the breaching of a loan covenant (technical default) entails a switch in investment control rights from borrowers to lenders. Lenders optimally choose low-risk projects, thus mitigating borrowers’ risk-taking incentives and lowering the cost of equity. This mechanism generates strong macroeconomic effects and mitigates the financial accelerator. Consistent with our model, proximity to technical default in the data is associated with 4.12% lower returns and lower exposure to systematic risk.

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