Economic Implications of Nonlinear Pricing Kernels
Management Science, vol 63, number 10, 2017
41 Pages Posted: 25 Mar 2008 Last revised: 1 Jun 2021
Date Written: March 1, 2016
Based on a family of discrepancy functions, we derive nonparametric stochastic discount factor (SDFs) bounds that naturally generalize variance (Hansen and Jagannathan, 1991), entropy (Backus, Chernov and Martin, 2011), and higher-moment (Snow, 1991) 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.
Keywords: Stochastic Discount Factor, Information-Theoretic Bounds, Generalized Minimum Contrast Estimators, Implicit Utility Maximizing Weights
JEL Classification: C1, C5, G1
Suggested Citation: Suggested Citation