The 'out-of-sample' performance of long run risk models☆

Wayne Ferson; Suresh Nallareddy; Biqin Xie

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The 'out-of-sample' performance of long run risk models☆

机译：长期风险模型的“样本外”性能☆

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This paper studies the ability of long-run risk models to explain out-of-sample asset returns during 1931-2009. The long-run risk models perform relatively well on the momentum effect.A cointegrated version of the model outperforms the classical, stationary version. Both the long-run and the short-run consumption shocks in the models are empirically important for the models' performance. The models' average pricing errors are especially small in the decades from the 1950s to the 1990s. When we restrict the risk premiums to identify structural parameters, this results in larger average pricing errors but often smaller error variances. The mean squared errors are not substantially better than those of the classical CAPM, except for Momentum.

机译：本文研究了长期风险模型解释1931-2009年样本外资产收益的能力。长期风险模型在动量效应方面表现相对较好，模型的协整版本优于经典的固定版本。在模型上，长期和短期的消费冲击都对模型的性能具有重要的经验意义。从1950年代到1990年代的几十年中，模型的平均定价误差特别小。当我们限制风险溢价以识别结构参数时，这会导致较大的平均定价误差，但通常会产生较小的误差方差。除动量外，均方差并没有比传统的CAPM更好。

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来源
《Journal of financial economics》 |2013年第3期|537-556|共20页
作者
Wayne Ferson; Suresh Nallareddy; Biqin Xie;
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作者单位

Marshall School of Business, University of Southern California, Los Angeles, CA 90089, USA;

Columbia Business School, Columbia University, New York, NY 10027, USA;

Smeal College of Business, Pennsylvania State University, State College, PA 16802, USA;

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正文语种 eng
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关键词
long-run risk models; out-of-sample;

机译：长期风险模型;样本外;

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The 'out-of-sample' performance of long run risk models☆

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