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...

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However,...

This paper examines the properties of the variance risk premium (VRP). We propose a flexible asset pricing model that captures co-jumps in prices and volatility, and self-exciting jump clustering. We estimate the model on equity returns and variance swap rates at different horizons. The total...

Investors demand higher premiums from firms whose future performance in R&D is difficult to evaluate. We construct a measure that captures investors' evaluation of a firm's R&D information quality (RDIQ) by linking a firm's historical innovation input (R&D expenditures) and innovation outcome...

We find a positive relationship between individual downside variance premia, the difference between risk-neutral and physical expected downside variances, and future corporate bond returns. The hedge portfolio earns the economically substantial and statistically significant excess return of...

This paper investigates the return-volatility relation by taking into account the model specification problem. The market volatility is modeled to have two components, one due to the diffusion risk and the other due to the jump risk. The model indicates that under the absence of leverage...