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.
Cited by
26 articles.
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