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...

In state-space models, parameter learning is practically difficult and is still an open issue. This paper proposes an efficient simulation-based parameter learning method. First, the approach breaks up the interdependence of the hidden states and the static parameters by marginalizing out 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,...

Relying on a latent factor model with time-varying temporal dependence of systematic risk and mispricing on firm and option characteristics, we reveal economically substantial mispricing in the options market. The portfolio based on individual options alphas related to characteristics earns an...

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...