A Frequency Decomposition of Approximation Errors in Stochastic Discount Factor Models
扩展了Hansen和Jagannathan的方法，将随机贴现因子模型中的近似误差按频率分解，并应用于多个消费型贴现因子模型，发现只有高风险厌恶的模型在低频上拟合较好。
This article extends the work of Hansen and Jagannathan by showing how to decompose approximation errors in stochastic discount factor models by frequency. This decomposition is applied to a number of consumption‐based discount factor models in order to investigate how well they fit at low frequencies. There is some evidence of improved fit at low frequencies, but only in models with high degrees of risk aversion. In models with low degrees of risk aversion, approximation errors at low frequencies are just as severe as those at high frequencies.