Robust Inference for Consumption-Based Asset Pricing

• 成果类型: Article
• 署名作者: Kleibergen, Frank; Zhan, Zhaoguo
• 署名单位: University of Amsterdam; University System of Georgia; Kennesaw State University
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.12855
• 发表日期: 2020
• 页码: 507-550
• 关键词: risk premia; MIMICKING PORTFOLIOS; generalized-method; cross-section; models; identification; tests; gmm; approximations; parameters

摘要

The reliability of traditional asset pricing tests depends on: (i) the correlations between asset returns and factors; (ii) the time series sample size T compared to the number of assets N. For macro-risk factors, like consumption growth, (i) and (ii) are often such that traditional tests cannot be trusted. We extend the Gibbons-Ross-Shanken statistic to test identification of risk premia and construct their 95% confidence sets. These sets are wide or unbounded when T and N are close, but show that average returns are not fully spanned by betas when T exceeds N considerably. Our findings indicate when meaningful empirical inference is feasible.