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- Title
Testing High-Dimensional Linear Asset Pricing Models.
- Authors
Lan, Wei; Feng, Long; Luo, Ronghua
- Abstract
We propose in this article a new procedure, based on random projections, for testing widely used linear asset pricing models (Sharpe, 1964; Linter, 1965; Fama and French, 1993). This new testing procedure is particularly suitable when the number of assets N is much larger than the number of observations T, and outperforms the existing methods by admitting the covariance matrix of the idiosyncratic term to be nonsparse. Under some mild conditions, we show theoretically that the test statistic is asymptotically normal as long as min {N; T} goes to infinity. The finite sample performance is investigated by extensive Monte Carlo experiments. The practical utility of the new testing procedure is further justified by treating the U.S. stock market. Employing this new testing procedure, we found that the Fama-French (FF) threefactor model (Fama and French, 1993) is better than the capital asset pricing model (Sharpe, 1964) in explaining the mean-variance efficiency of the U.S. stock market.
- Subjects
CAPITAL assets pricing model; HIGH-dimensional model representation; EFFICIENT market theory; RANDOM projection method; COVARIANCE matrices
- Publication
Journal of Financial Econometrics, 2018, Vol 16, Issue 2, p191
- ISSN
1479-8409
- Publication type
Article
- DOI
10.1093/jjfinec/nby002