Economic Implications of Nonlinear Pricing Kernels
基于差异函数族推导非参数随机贴现因子边界，推广了方差、熵和高阶矩边界，用于识别资产定价模型中定价核离散度的来源（偏度或峰度），并检验灾难、失望厌恶和长期风险模型的可接受性。
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.