Sellers of variance swaps earn time-varying risk premia for their exposure to realized variance, the level of variance swap rates, and the slope of the variance swap curve. To measure risk premia, we estimate a dynamic term structure model that decomposes variance swap rates into expected...

The long-run consumption risk model provides a theoretically appealing explanation for prominent asset pricing puzzles, but its intricate structure presents a challenge for econometric analysis. This paper proposes a two-step indirect inference approach that disentangles the estimation of the...

This paper uses wavelets to decompose each stock's trading-volume variance into frequency-specific components. We find that stocks dominated by short-run fluctuations in trading volume have abnormal returns that are 1% per month higher than otherwise similar stocks where short-run fluctuations...

This paper analyzes a term structure model that allows for both stochastic correlation between underlying factors and an extended market price of risk specification. We show that significant improvement in bond fitting and portfolio performance is obtained by the model. However, the restriction...

This empirical paper comprehensively sets out the impact of underspecification on a key foundational concept in empirical finance, the linear factor model. It places emphasis on the extensive consequences of factor omission for model estimation and interpretation. Factor omission in time-series...

This paper estimates the term-structure of volatility risk premia for the stock market. Realized variance term premia are increasing in systematic risk and predict variance swap returns. Implied volatility term premia are decreasing in risk initially, but then increase at a lag, predicting VIX...

For every U.S.-listed security for every year between 2001-2017, I run four different event studies to calculate four separate objective measures of the efficiency of the market for that security for that year, which provide an objective characterization of the market for that security in that...

We directly optimize portfolio weights as a function of firm characteristics via deep neural networks by generalizing the parametric portfolio policy framework. Our results show that network-based portfolio policies result in an increase of investor utility of between 30 and 100 percent over a...

This paper aims to characterize the time series dynamics of asset price under the statistical probability measure in the presence of bubbles (defined according to the local martingale theory). We advocate that quadratic-variation risk premium can serve as a mechanism leading to forward-looking...