A Unifying Approach to the Empirical Evaluation of Asset Pricing Models
证明连续更新GMM等单步估计器在评估线性因子定价模型时，无论模型是否有效、因子是否可交易，都能得到相同的风险价格、定价误差和过度识别检验结果，并用货币收益数据和蒙特卡洛模拟验证。
Regression and SDF approaches with centered or uncentered moments and symmetric or asymmetric normalizations are commonly used to empirically evaluate linear factor pricing models. We show that unlike two-step or iterated GMM procedures, single-step estimators such as continuously updated GMM yield numerically identical risk prices, pricing errors, and overidentifying restrictions tests irrespective of the model validity and regardless of the factors being traded, or the use of excess or gross returns. We illustrate our results with Lustig and Verdelhan’s (2007) currency returns, propose tests to detect some problematic cases, and provide Monte Carlo evidence on the reliability of asymptotic approximations. © 2015 The President and Fellows of Harvard College and the Massachusetts Institute of Technology