Economic Implications of Nonlinear Pricing Kernels
Getulio Vargas Foundation
EDHEC Business School
December 23, 2014
AFA 2009 San Francisco Meetings Paper
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.
Number of Pages in PDF File: 37
Keywords: Stochastic Discount Factor, Information-Theoretic Bounds, Generalized Minimum Contrast Estimators, Implicit Utility Maximizing Weights
JEL Classification: C1, C5, G1
Date posted: March 25, 2008 ; Last revised: December 24, 2014
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