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• W2261360823 abstract "This paper proposes a data-based measure of model performance to discriminate among competing asset pricing models of return predictability. I form a set of variance bounds on pricing kernels based on different systems for predicting asset returns. For a given asset pricing model, I define the robust variance bound to be the tightest variance bound that this model-implied pricing kernel is able to satisfy. Using the diagnostic results of the robust variance bounds, I then construct a model performance index. This index quantifies the degree of return predictability which a given asset pricing model is able to obtain. I apply this method to examine the performance of three leading classes of asset pricing models: long run risk, external habit and rare disasters. The long run risk type of rare disaster model of Nakamura et al. (2013) performs best." @default.
• W2261360823 created "2016-06-24" @default.
• W2261360823 creator A5062456086 @default.
• W2261360823 date "2014-01-01" @default.
• W2261360823 modified "2023-09-27" @default.
• W2261360823 title "A Robust Variance Bound on Pricing Kernels" @default.
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• W2261360823 doi "https://doi.org/10.2139/ssrn.2513193" @default.
• W2261360823 hasPublicationYear "2014" @default.
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