Modeling non-normality using multivariate t: implications for asset pricing. Issue 1 (20th February 2017) - Kan, Raymond; Zhou, Guofu
Abstract : Purpose: The purpose of this paper is to show that multivariate t -distribution assumption provides a better description of stock return data than multivariate normality assumption. Design/methodology/approach: The EM algorithm is applied to solve the statistical estimation problem almost analytically, and the asymptotic theory is provided for inference. Findings: The authors find that the multivariate normality assumption is almost always rejected by real stock return data, while the multivariate t -distribution assumption can often be adequate. Conclusions under normality vs under t can be drastically different for estimating expected returns and Jensen's α s, and for testing asset pricing models. Practical implications: The results provide improved estimates of cost of capital and asset moment parameters that are useful for corporate project evaluation and portfolio management. Originality/value: The authors proposed new procedures that makes it easy to use a multivariate t -distribution, which models well the data, as a simple and viable alternative in practice to examine the robustness of many existing results.