Testing Beta-Pricing Models Using Large Cross-Sections

• 成果类型: Article
• 署名作者: Raponi, Valentina; Robotti, Cesare; Zaffaroni, Paolo
• 署名单位: Imperial College London; University of Warwick
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhz064
• 发表日期: 2020
• 页码: 2796
• 关键词: DISCOUNT FACTOR MODELS; risk premia; MIMICKING PORTFOLIOS; Robust Inference; performance; arbitrage; identification; returns

摘要

We propose a methodology for estimating and testing beta-pricing models when a large number of assets is available for investment but the number of time-series observations is fixed. We first consider the case of correctly specified models with constant risk premia, and then extend our framework to deal with time-varying risk premia, potentially misspecified models, firm characteristics, and unbalanced panels. We show that our large cross-sectional framework poses a serious challenge to common empirical findings regarding the validity of beta-pricing models. In the context of pricing models with Fama-French factors, firm characteristics are found to explain a much larger proportion of variation in estimated expected returns than betas.