Economic Implications of Nonlinear Pricing Kernels

• 成果类型: Article
• 署名作者: Almeida, Caio; Garcia, Rene
• 署名单位: Getulio Vargas Foundation; Universite Catholique de Lille; EDHEC Business School; Universite de Montreal; Universite de Montreal
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2016.2498
• 发表日期: 2017
• 页码: 3361-3380
• 关键词: Stochastic discount factors; information-theoretic bounds; Robustness; minimum contrast estimators; implicit utility maximizing weights

摘要

Based on a family of discrepancy functions, we derive nonparametric stochastic discount factor bounds that naturally generalize variance, entropy, and higher-moment bounds. These bounds are especially useful to identify how parameters affect pricing kernel dispersion in asset pricing models. In particular, they allow us to distinguish between models where dispersion comes mainly from skewness from models where kurtosis is the primary source of dispersion. We analyze the admissibility of disaster, disappointment aversion, and long-run risk models with respect to these bounds.