Economic implications of nonlinear pricing kernels

Caio Almeida, René Garciab

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

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 a ect 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.

Original languageEnglish (US)
Pages (from-to)3361-3380
Number of pages20
JournalManagement Science
Volume63
Issue number10
DOIs
StatePublished - Oct 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research

Keywords

  • Implicit utility maximizing weights
  • Information-theoretic bounds
  • Minimum contrast estimators
  • Robustness
  • Stochastic discount factors

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