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Too good to be true? Fallacies in evaluating risk factor models

机译:难以置信?评估风险因素模型的谬误

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This paper is concerned with statistical inference and model evaluation in possibly misspecified and unidentified linear asset pricing models estimated by maximum likelihood. Strikingly, when spurious factors (that is, factors that are uncorrelated with the returns on the test assets) are present, the model exhibits perfect fit, as measured by the squared correlation between the model's fitted expected returns and the average realized returns. Furthermore, factors that are spurious are selected with high probability, and factors that are useful are driven out of the model. While ignoring potential misspecification and lack of identification can be very problematic for models with macroeconomic factors, empirical specifications with traded factors (e.g., Fama and French, 1993; Hou et al., 2015) do not suffer from the identification problems shown in this study. (C) 2018 Elsevier B.V. All rights reserved.
机译:本文涉及在最大似然估计的可能错误指定和不确定的线性资产定价模型中的统计推断和模型评估。令人惊讶的是,当存在虚假因素(即与测试资产的收益不相关的因素)时,该模型表现出完美的拟合度,这是通过模型的拟合预期收益与平均已实现收益之间的平方相关性来衡量的。此外,虚假因素的选择率很高,有用的因素则被排除在模型之外。尽管对于具有宏观经济因素的模型而言,忽略潜在的错误指定和缺乏识别可能会带来很大的问题,但具有贸易因素的经验指标(例如,Fama和French,1993; Hou等,2015)不会遭受本研究中显示的识别问题的困扰。 。 (C)2018 Elsevier B.V.保留所有权利。

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