Modeling non-normality using multivariatet: implications for asset pricing

Purpose - The purpose of this paper is to show that multivariatet-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 multivariatet-distribution assumption can often be adequate. Conclusions under normality vs undertcan be drastically different for estimating expected returns and Jensen’sαs, 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 multivariatet-distribution, which models well the data, as a simple and viable alternative in practice to examine the robustness of many existing results.