基于因素模型的权益成本

R. Stambaugh, Ľuboš Pástor
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引用次数: 0

摘要

个别公司的资本权益成本是使用几个模型来估计的,这些模型将预期收益与一个或多个普遍因素的贝塔系数联系起来。贝叶斯方法结合了资产错误定价的先验不确定性以及贝塔和因子均值的不确定性。先前关于错误定价的大量不确定性导致估计的权益成本接近于排除错误定价的情况下获得的权益成本。平均而言,使用哪种定价模型的不确定性似乎不如模型内参数的不确定性重要。在不存在错误定价的不确定性的情况下,尽管贝塔的不确定性几乎同样重要,但因子均值的不确定性通常是企业权益成本总体不确定性的最重要来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Costs of Equity from Factor-Based Models
Equity costs of capital for individual firms are estimated using several models that relate expected returns to betas on one or more pervasive factors. A Bayesian approach incorporates prior uncertainty about an asset's mispricing as well as uncertainty about betas and factor means. Substantial prior uncertainty about mispricing results in an estimated cost of equity close to that obtained with mispricing ruled out. Uncertainty about which pricing model to use appears to be less important, on average, than within-model parameter uncertainty. In the absence of mispricing uncertainty, uncertainty about factor means is generally the most important source of overall uncertainty about a firm's cost of equity, although uncertainty about betas is nearly as important.
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