Relative Entropy and Minimum-Variance Pricing Kernel in Asset Pricing Model Evaluation.

ABSTRACT: Recent literature shows that many testing procedures used to evaluate asset pricing models result in spurious rejection probabilities. Model misspecification, the strong factor structure of test assets, or skewed test statistics largely explain this. In this paper we use the relative entropy of pricing kernels to provide an alternative framework for testing asset pricing models. Building on the fact that the law of one price guarantees the existence of a valid pricing kernel, we study the relationship between the mean-variance efficiency of a model's factor-mimicking portfolio, as measured by the cross-sectional generalized least squares (GLS) R 2 statistic, and the relative entropy of the pricing kernel, as determined by the Kullback-Leibler divergence. In this regard, we suggest an entro

SUBMITTER: Rojo-Suarez J 

PROVIDER: S-EPMC7517259 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature