Application of the Multivariate Average F-Test to Examine Relative Performance of Asset Pricing Models with Individual Security Returns

Shafiqur Rahman and Matthew J. Schneider

Chapter 10 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 391-430 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The standard multivariate test of Gibbons et al. (1989) used in studies examining relative performance of alternative asset pricing models requires the number of stocks to be less than the number of time-series observations, which requires stocks to be grouped into portfolios. This results in a loss of disaggregate stock information. We apply a new statistical test to get around this problem. We find that the multivariate average F-test developed by Hwang and Satchell (2014) has superior power to discriminate among competing models and does not reject tested models altogether, unlike the standard multivariate test. Application of the multivariate average F-test for examination of relative performance of asset pricing models demonstrate that a parsimonious 6-factor model with the market, size, orthogonal value, profitability, investment, and momentum factors outperforms all other models.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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