Economic Implications of Nonlinear Pricing Kernels
Management Science · 2017 · Volume 63 · Issue 10
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 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. This paper was accepted by Jerome Detemple, finance .
Keywords
stochastic discount factors, information-theoretic bounds, robustness, minimum contrast estimators, implicit utility maximizing weights
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