General Aggregation of Misspecified Asset Pricing Models

Nikolay Gospodinov and Esfandiar Maasoumi

Working Paper 2017-10
November 2017

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This paper proposes an entropy-based approach for aggregating information from misspecified asset pricing models. The statistical paradigm is shifted away from parameter estimation of an optimally selected model to stochastic optimization based on a risk function of aggregation across models. The proposed method relaxes the perfect substitutability of the candidate models, which is implicitly embedded in the linear pooling procedures, and ensures that the aggregation weights are selected with a proper (Hellinger) distance measure that satisfies the triangle inequality. The empirical results illustrate the robustness and the pricing ability of the aggregation approach to stochastic discount factor models.

JEL classification: C13, C52, G12

Key words: entropy, model aggregation, asset pricing, misspecified models, oracle inequality, Hellinger distance

The authors thank Mark Fisher, Ruixuan Liu, and the participants at the Max King Conference (Monash University) and the Econometrics Workshop (University of Kansas) for useful discussions and suggestions. The views expressed here are the authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility.
Please address questions regarding content to Nikolay Gospodinov, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, 404-498-7892,, or Esfandiar Maasoumi, Emory University, Department of Economics, Rich Memorial Building 324, 1602 Fishburne Drive, Atlanta, GA 30322-2240,
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