Econ Papers Daily / 期刊论文
检验线性因子模型中的阿尔法：一种投资组合方法
Testing Alphas in Linear Factor Models: A Portfolio Approach
摘要
This paper develops a test for evaluating linear factor models in high-dimensional settings. The proposed Maximum Random Sparse Portfolio (MRSP) test generates L random sparse portfolios, each obtained through K random draws, and then selects the hardest-to-price portfolio to assess factor model performance. By evaluating multiple randomly generated sparse portfolios, the MRSP test retains a portfolio-based economic interpretation while avoiding reliance on a single hardest-to-price asset. Under the null hypothesis and the stated regularity conditions, the centered MRSP statistic converges to a type I extreme value distribution. Monte Carlo simulations show stable finite-sample performance. In the empirical application, model fit generally improves in recent years, although the evidence varies across factor models and test assets. The 3-factor model of Daniel, Hirshleifer, and Sun (2020, Review of Financial Studies ) performs best among the models considered. Keywords: Asset pricing test high-dimensional assets model comparison random sparse portfolio max-type test Disclaimer As a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content? Academic Permissions Obtain permissions instantly via Rightslink by clicking on the button below: Request Academic Permissions For more information, please visit our Permissions help page. Corporate Permissions To request corporate permissions for this article, please click on the button below: Request Corporate Permissions Reprints To request a reprint for this article, please click on the button below: Order Reprints Share Back to Top
中文摘要
本文提出了一种在高维情形下评估线性因子模型的检验方法。所提出的最大随机稀疏投资组合（MRSP）检验生成L个随机稀疏投资组合，每个投资组合通过K次随机抽取得到，然后选择最难定价的投资组合来评估因子模型表现。通过评估多个随机生成的稀疏投资组合，MRSP检验保留了基于投资组合的经济学解释，同时避免依赖单一最难定价资产。在原假设和所述正则条件下，中心化MRSP统计量收敛于I型极值分布。蒙特卡洛模拟显示其有限样本表现稳定。在实证应用中，模型拟合近年来总体有所改善，尽管证据因因子模型和检验资产而异。Daniel、Hirshleifer和Sun（2020，Review of Financial Studies）的三因子模型在所考虑的模型中表现最佳。关键词：资产定价检验 高维资产 模型比较 随机稀疏投资组合 极大型检验 免责声明：作为向作者和研究人员提供的一项服务，我们提供的是已接受稿件（AM）的这一版本。在最终出版记录版本（VoR）之前，将对该稿件进行文字编辑、排版以及生成校样。在生产制作和印前阶段，可能会发现影响内容的错误，所有与该期刊相关的法律免责声明也适用于这些版本。请注意：选择许可并不提供对文章全文的访问权限，请参阅我们的帮助页面“如何查看内容？” 学术许可：通过Rightslink点击下方按钮可立即获得许可：申请学术许可。更多信息，请访问我们的许可帮助页面。企业许可：如需申请本文的企业许可，请点击下方按钮：申请企业许可。转载：如需申请本文转载，请点击下方按钮：订购转载。分享 返回顶部