Maxing Out Entropy: A Conditioning Approach

58 Pages Posted: 26 Oct 2020 Last revised: 9 Nov 2020

See all articles by Fousseni Chabi-Yo

Fousseni Chabi-Yo

University of Massachusetts Amherst - Isenberg School of Management

Yan Liu

Purdue University

Date Written: September 13, 2020

Abstract

Entropy and entropy-like measures of pricing kernel dispersion emerge as useful tools in asset pricing research. We develop a systematic approach to bounding entropy by incorporating conditioning information. Our bounds feature a fixed-point solution to a dynamic asset allocation problem. Similar to how the Sharpe ratio provides variance bound in the L2-space, our solution is interpret-able as generalized "Sharpe ratios" in the entropy space. Importantly, as opposed to relying on physical moments alone, our bounds strike a balance in exploiting physical return predictability and hedging risk-neutral higher order moments. Applying our approach to recently proposed return predictors, we document enhanced entropy restrictions that more than double the benchmark equity risk premium. We highlight the implications of our results in diagnosing leading macro-finance models.

Keywords: Conditioning Information, Equity Risk Premium, Risk-Neutral Moments, Preferences, Entropy, Model-Free, Stochastic Discount Factor

JEL Classification: E44, G1, G12, G13

Suggested Citation

Chabi-Yo, Fousseni and Liu, Yan, Maxing Out Entropy: A Conditioning Approach (September 13, 2020). Available at SSRN: https://ssrn.com/abstract=3691907 or http://dx.doi.org/10.2139/ssrn.3691907

Fousseni Chabi-Yo

University of Massachusetts Amherst - Isenberg School of Management ( email )

Amherst, MA 01003-4910
United States

Yan Liu (Contact Author)

Purdue University ( email )

West Lafayette, IN 47907-1310
United States

HOME PAGE: http://yliu1.com

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