Stochastic Discount Factor Bounds with Conditioning Information
研究了使用条件信息时Hansen-Jagannathan随机贴现因子边界的抽样性质，发现有限样本偏差会导致资产定价模型被过度拒绝，并提供了偏差修正方法。
Hansen and Jagannathan (1991) (hereafter HJ) derive restrictions on the volatility of stochastic discount factors that price a given set of returns. This article studies the sampling properties of HJ bounds that use conditioning information. One approach is to multiply the returns by the lagged variables. We also study optimized HJ bounds with conditioning information from Gallant, Hansen, and Tauchen (1990) and based on portfolios derived in Ferson and Siegel (2001). We document striking finite-sample biases in the HJ bounds, where the bounds reject asset-pricing models too often. We provide a useful bias correction. We also evaluate asymptotic standard errors for the bounds from Hansen, Heaton, and Luttmer (1995). Copyright 2003, Oxford University Press.