Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities
Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities
复制标题
具有大量证券的线性因子定价模型中的 Alpha 测试
DOI：
10.1093/jjfinec/nbad002
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发表时间：
2023
影响因子：
2.5
通讯作者：
Yamagata Takashi
中科院分区：
文献类型：
--
作者：
Pesaran M Hashem;Yamagata Takashi
本文考虑当证券数量N远大于单个收益序列的时间维度T时，线性因子定价模型中的α检验。我们专注于类的测试，是基于学生的st测试的个别证券，有一些优势，超过现有的标准化Wald类型的测试，并提出了一个测试程序，允许非高斯和一般形式的弱交叉相关的错误。它不需要估计一个可逆的误差协方差矩阵，它是快速实现，即使N是有效的比T大得多。我们还表明，建议的测试可以解释一定程度的定价误差允许在罗斯的套利定价理论条件下。Monte Carlo证据表明，即使当T =60和N =5000时，所提出的测试也表现得非常好。该测试适用于标准普尔500指数证券在每个月底的真实的月度回报率，使用大小为60的滚动窗口。对Sharpe-Lintner资本资产定价模型和Fama-French三因素和五因素模型的统计显著性证据主要在大衰退期间（2007年M12 - 2009年M06）发现。
This article considers tests of alpha in linear factor pricing models when the number of securities,N, is much larger than the time dimension,T, of the individual return series. We focus on class of tests that are based on Student’st-tests of individual securities which have a number of advantages over the existing standardized Wald type tests, and propose a test procedure that allows for non-Gaussianity and general forms of weakly cross-correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even ifNis much larger thanT. We also show that the proposed test can account for some limited degree of pricing errors allowed under Ross’s arbitrage pricing theory condition. Monte Carlo evidence shows that the proposed test performs remarkably well even whenT=60 andN=5000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically significant evidence against Sharpe–Lintner capital asset pricing model and Fama–French three and five factor models are found mainly during the period of Great Recession (2007M12–2009M06).