Published Online:https://doi.org/10.1287/mnsc.2016.2498

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.

INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.