Modeling non-normality using multivariatet: implications for asset pricing

Raymond Kan and Guofu Zhou

China Finance Review International, 2017, vol. 7, issue 1, 2-32

Abstract: 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.

Keywords: Asset pricing; α; Cost of capital; Normality; t-Distribution; C12; C13; G12 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:cfripp:cfri-10-2016-0114

DOI: 10.1108/CFRI-10-2016-0114

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