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比较资产定价模型的距离度量

文章 arXiv papers · 作者: Zhongzhi Lawrence He
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总结

本文提出基于距离的指标，用于比较资产定价模型。其出发点是统计检验的检验力可能较低，而且对阿尔法统计量的使用可能缺乏规范。所提指标将阿尔法值的平方与标准误的平方相结合，再取平方根，从而用一个诊断指标同时考虑估计的错误定价和不确定性。

本文还给出一种贝叶斯解释：模型表现被视为模型隐含分布与代表怀疑态度的数据分布之间的距离。按照这一解释，阿尔法离散程度较低、解释力较强的模型更受青睐。描述称，在Fama-French五因子模型中加入动量因子，可以缓解其所述的年度错误定价介于负百分之八至正百分之八的问题。这些指标旨在补充而非取代p值；所提供文本未给出方法细节或更广泛的验证。

核心观点

  • 所提指标将阿尔法值平方和标准误平方结合为一个距离度量。
  • 贝叶斯框架将模型隐含信念与基于数据、体现怀疑态度的分布进行比较。
  • 该论述更青睐阿尔法离散程度低、解释力强的模型。
  • 论文认为，动量因子对Fama-French五因子模型有所补充。
  • 距离指标旨在补充频率学派的p值，作为模型诊断工具。

标签

因子投资统计学动量
全文
# Comparing Asset Pricing Models: Distance-based Metrics and Bayesian Interpretations


# Comparing Asset Pricing Models: Distance-based Metrics and Bayesian Interpretations









In light of the power problems of statistical tests and undisciplined use of alpha-based statistics to compare models, this paper proposes a unified set of distance-based performance metrics, derived as the square root of the sum of squared alphas and squared standard errors. The Bayesian investor views model performance as the shortest distance between his dogmatic belief (model-implied distribution) and complete skepticism (data-based distribution) in the model, and favors models that produce low dispersion of alphas with high explanatory power. In this view, the momentum factor is a crucial addition to the five-factor model of Fama and French (2015), alleviating his prior concern of model mispricing by -8% to 8% per annum. The distance metrics complement the frequentist p-values with a diagnostic tool to guard against bad models.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。