Modeling non-normality using multivariate t : implications for asset pricing
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 αs, 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.
Keywords
Acknowledgements
The authors are grateful for helpful discussions and comments of Giovanni Barone-Adesi, Martijn Cremers, Anna Dodonova, Heber Farnsworth, Wayne Ferson, Kenneth French, William Goetzmann, Campbell Harvey, Yongmiao Hong, Ravi Jagannathan, Christopher Jones, Lynda Khalaf, Bruce Lehmann, Canlin Li, Andrew Lo, L̆uboš Pástor, Jay Shanken, Jun Tu, seminar participants at the Vanderbilt University and the Washington University in St Louis, and participants at the 2003 Northern Finance Meetings and the 2006 China International Conference in Finance. The authors are also grateful to the valuable comments of an anonymous referee and an editor. Kan gratefully acknowledges financial support from the Social Sciences and Humanities Research Council of Canada.
Citation
Kan, R. and Zhou, G. (2017), "Modeling non-normality using multivariate
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited