The relationship between risk and return is one of the most studied topics in finance. The majority of the literature is based on a linear, parametric relationship between expected returns and conditional volatility. This paper models the contemporaneous relationship between market excess...

We show that a business-cycle component of consumption growth (dubbed business-cycle consumption) with cycles between 2 and 4 years is effective in explaining the differences in risk premia across alternative test assets, including recently-proposed anomaly portfolios. We formalize the mapping...

By using a nonlinear VAR model, we investigate whether the response of the US stock and housing markets to uncertainty shocks depends on financial conditions. Our model allows us to change the response of the US financial markets to volatility shocks in periods of normal and financial distress....

By using a nonlinear VAR model, we investigate whether the response of the US stock and housing markets to uncertainty shocks depends on financial conditions. Our model allows us to change the response of the US financial markets to volatility shocks in periods of normal and financial distress....

The relationship between risk and return is one of the most studied topics in finance. The majority of the literature is based on a linear, parametric relationship between expected returns and conditional volatility. This paper models the contemporaneous relationship between market excess...

This study examines the association between chief executive officer (CEO) overconfidence and future stock price crash risk. Overconfident managers overestimate the returns to their investment projects and misperceive negative net present value (NPV) projects as value creating. They also tend to...

I use classification-based machine-learning methods to decompose 32 anomaly payoffsinto risk exposures and mispricing. The component driven by risk earns statistically insignificantreturns, despite its efficacy in explaining the time-series variation in anomaly payoffs.The mispricing component...

I develop a model of statistical arbitrage trading in an environment with "fat-tailed" information. If risk-neutral arbitrageurs are uncertain about the variance of fat-tail shocks and if they implement max-min robust optimization, they will choose to ignore a wide range of pricing errors....