Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/200522 
Year of Publication: 
2017
Series/Report no.: 
Working Paper No. 2017-10
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, Ga.
Abstract: 
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.
Subjects: 
entropy
model aggregation
asset pricing
misspecified models
oracle inequality
Hellinger distance
JEL: 
C13
C52
G12
Document Type: 
Working Paper

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