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A UNIFYING APPROACH TO THE EMPIRICAL EVALUATION OF ASSET PRICING MODELS

机译:资产定价模型实证评价的唯一方法

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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.
机译:具有中心矩或非中心矩以及对称或不对称归一化的回归和SDF方法通常用于根据经验评估线性因子定价模型。我们表明,与两步或迭代GMM程序不同,单步估计器(例如,不断更新的GMM)在数值上具有相同的风险价格,定价错误以及过度识别限制测试,而与模型的有效性,交易的因素或使用无关。超额收益或总收益。我们用Lustig和Verdelhan(2007)的货币收益率来说明我们的结果,提出测试以检测一些有问题的情况,并提供关于渐近近似的可靠性的蒙特卡洛证据。

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