New Entropy Restrictions and the Quest for Better-Specified Asset-Pricing Models

Abstract

This article proposes the entropy of m2 (m is the stochastic discount factor) as a metric to evaluate asset-pricing models. We develop a bound on the entropy of m2 when m correctly prices a finite number of returns and consider models that pass the lower bound on m, yet fail the lower bound on m2. Interpreting our results, we elaborate on the distinction between the entropy of m2 versus the entropy of m. We further show that the entropy of m2 represents an upper bound on the expected excess (log) return of the security with the payoff of m.

Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory

“…In our work the movements in skewness are not simply driven by rare events and provide directional information about either up-or downside risks. Finally, our work is related to Chabi-Yo (2012) and Bakshi and Chabi-Yo (2014) that have pointed out the relevance of higher order moments for the construction of tighter bounds on the variance of pricing kernels.…”