Density selection and combination under model ambiguity: an application to stock returns

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Federal Reserve Board, Divisions of Research & Statistics and Monetary Affairs, 2005 - Business & Economics - 48 pages
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The paper shows that the KLD between the nonparametric and the parametric density estimates is asymptotically normally distributed. This result leads to determining the weights in the model combination, using the distribution function of a Normal centered on the average performance of all plausible models. Consequently, the final weight is determined by the ability of a given model to perform better than the average. As such, this combination technique does not require the true structure to belong to the set of competing models and is computationally simple. I apply the proposed method to estimate the density function of daily stock returns under different phases of the business cycle. The results indicate that the double Gamma distribution is superior to the Gaussian distribution in modeling stock returns, and that the combination outperforms each individual candidate model both in- and out-of-sample"--Abstract.

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