A Robust Variance Bound on Pricing Kernels

53 Pages Posted: 29 Nov 2014 Last revised: 4 Dec 2014

Date Written: November 22, 2014

Abstract

This paper proposes a data-based measure of model performance to discriminate among competing asset pricing models of return predictability. I form a set of variance bounds on pricing kernels based on different systems for predicting asset returns. For a given asset pricing model, I define the robust variance bound to be the tightest variance bound that this model-implied pricing kernel is able to satisfy. Using the diagnostic results of the robust variance bounds, I then construct a model performance index. This index quantifies the degree of return predictability which a given asset pricing model is able to obtain. I apply this method to examine the performance of three leading classes of asset pricing models: long run risk, external habit and rare disasters. The long run risk type of rare disaster model of Nakamura et al. (2013) performs best.

Keywords: returns predictability, robust variance bound on pricing kernels, asset pricing models

JEL Classification: G12, G11, E44, E37, C11

Suggested Citation

Yang, Haoxi, A Robust Variance Bound on Pricing Kernels (November 22, 2014). Available at SSRN: https://ssrn.com/abstract=2513193 or http://dx.doi.org/10.2139/ssrn.2513193

Haoxi Yang (Contact Author)

Nankai University ( email )

Tongyan Road 38
Tianjin, Tianjin 300350
China

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