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 whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we...

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 stocks' implied variance asymmetry, defined as the difference between upside and downside risk-neutral semivariances extracted from out-of-money options, and future stock returns. The high-minus-low hedge portfolio earns the excess return of...