高维要素定价模型中alpha的自适应检验

IF 2.9 2区 数学 Q1 ECONOMICS Journal of Business & Economic Statistics Pub Date : 2023-06-29 DOI:10.1080/07350015.2023.2217871
Qiang Xia, Xianyang Zhang
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引用次数: 0

摘要

本文提出了一种新的方法,通过检验具有大量资产的线性因素定价模型中α的存在来验证多因素定价理论。由于市场的无效定价很可能发生在一小部分特殊资产上,因此我们开发了一种针对稀疏信号特别强大的测试程序。基于高维高斯近似理论,我们提出了一种基于仿真的方法来近似检验的极限零分布。我们的数值研究表明,与稀疏替代方案下的现有测试相比,新程序可以提供合理的尺寸并实现显着的功率改进,特别是对于弱信号。
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Adaptive Testing for Alphas in High-Dimensional Factor Pricing Models
This article proposes a new procedure to validate the multi-factor pricing theory by testing the presence of alpha in linear factor pricing models with a large number of assets. Because the market’s inefficient pricing is likely to occur to a small fraction of exceptional assets, we develop a testing procedure that is particularly powerful against sparse signals. Based on the high-dimensional Gaussian approximation theory, we propose a simulation-based approach to approximate the limiting null distribution of the test. Our numerical studies show that the new procedure can deliver a reasonable size and achieve substantial power improvement compared to the existing tests under sparse alternatives, and especially for weak signals.
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来源期刊
Journal of Business & Economic Statistics
Journal of Business & Economic Statistics 数学-统计学与概率论
CiteScore
5.00
自引率
6.70%
发文量
98
审稿时长
>12 weeks
期刊介绍: The Journal of Business and Economic Statistics (JBES) publishes a range of articles, primarily applied statistical analyses of microeconomic, macroeconomic, forecasting, business, and finance related topics. More general papers in statistics, econometrics, computation, simulation, or graphics are also appropriate if they are immediately applicable to the journal''s general topics of interest. Articles published in JBES contain significant results, high-quality methodological content, excellent exposition, and usually include a substantive empirical application.
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