Evaluating the Predictive Abilities of Semiparametric Multivariate Models

Valentyn Panchenko

No 382, Computing in Economics and Finance 2006 from Society for Computational Economics

Abstract: We propose a new semiparametric procedure for estimating multivariate models with conditioning variables. The semiparametric model is based on the parametric conditional copula and nonparametric conditional marginals. To avoid the curse of dimensionality in the estimation of the latter, we propose a dimension reduction technique. The marginals are estimated using conditional kernel smoothers based on local linear estimator. The semiparametric copula model is compared with the parametric DCC model using predictive likelihood as a criterion. The comparison is based on the recent conditional test for predictive abilities. We use various simulations and financial series to compare the methods and show when the proposed semiparametric model is expected to be superior to the fully parametric DCC model

Keywords: risk management; copula; correlation; multivariate time series; nonparametric conditional distribution (search for similar items in EconPapers)
JEL-codes: C12 C51 G15 (search for similar items in EconPapers)
Date: 2006-07-04
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:382

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