Economic Implications of Nonlinear Pricing Kernels

Caio Almeida () and René Garcia
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Caio Almeida: FGV/EPGE–Escola Brasileira de Economia e Finanças, Rio de Janeiro, Brazil

Management Science, 2017, vol. 63, issue 10, 3361-3380

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.

Keywords: stochastic discount factors; information-theoretic bounds; robustness; minimum contrast estimators; implicit utility maximizing weights (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:63:y:2017:i:10:p:3361-3380

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