Robust Economic Implications of Nonlinear Pricing Kernels
Getulio Vargas Foundation
EDHEC Business School
August 5, 2013
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 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 and long-run risk models with respect to these bounds and gain important new insights on the role played by non-linearities in the pricing kernels and non-Gaussian features in the returns. Our bounds impose a data-driven balance between the amount of skewness and kurtosis that any admissible pricing kernel should satisfy.
Number of Pages in PDF File: 53
Keywords: Stochastic Discount Factor, Information-Theoretic Bounds, Generalized Minimum Contrast Estimators, Implicit Utility Maximizing Weights
JEL Classification: C1, C5, G1working papers series
Date posted: March 25, 2008 ; Last revised: August 8, 2013
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