Density selection and combination under model ambiguity: an application to stock returns
Federal Reserve Board, Divisions of Research & Statistics and Monetary Affairs, 2005 - Business & Economics - 48 pages
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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Adam Copeland affect the asymptotic Ana Aizcorbe Analysis Andreas Lehnert Andrew Cohen Application to Stock approximation error April asset returns assumption asymptotic distribution asymptotically normally distributed Athanasios Orphanides August Bayesian Berger Brian Sack Business Cycle candidate models Chris Downing competing models conﬁrm Covitz December deﬁne deﬁnition double Gamma Econometrics Effects Egon Zakrajsek Entire sample Entropy equal excess Kurtosis Expansion Contraction February Federal Reserve ﬁnal Financial ﬁnite ﬁrst term Gamma distribution Gaussian distribution in-sample Inﬂation Interest Rates January July June Kernel Kevin Moore Kullback-Leibler distance Lemma Market Measures model ambiguity model combination model misspeciﬁcation Monetary Policy nonparametric density estimate Nonparametric Estimation nonparametric ﬁt normally distributed November obtain October optimal out-of-sample performance paper parametric models plausible models probability receives a weight regimes September 2002 set of candidate Skewness speciﬁc Statistics Steven Stock Return Predictability Takeshi Kimura Theorem true model true structure variance Wayne Passmore