Out-of-sample comparison of copula specifications in multivariate density forecasts
AbstractWe introduce a statistical test for comparing the predictive accuracy of competing copula specifications in multivariate density forecasts, based on the Kullback-Leibler Information Criterion (KLIC). The test is valid under general conditions: in particular it allows for parameter estimation uncertainty and for the copulas to be nested or nonnested. Monte Carlo simulations demonstrate that the proposed test has satisfactory size and power properties in finite samples. Applying the test to daily exchange rate returns of several major currencies against the US dollar we find that the Student’s t copula is favored over Gaussian, Gumbel and Clayton copulas. This suggests that these exchange rate returns are characterized by symmetric tail dependence.
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Bibliographic InfoPaper provided by School of Economics, The University of New South Wales in its series Discussion Papers with number 2008-23.
Length: 27 pages
Date of creation: Oct 2008
Date of revision:
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Copula-based density forecast; semiparametric statistics; out-of-sample forecast evaluation; Kullback-Leibler Information Criterion; empirical copula;
Other versions of this item:
- Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
- Cees Diks & Valentyn Panchenko & Dick van Dijk, . "Out-of-sample Comparison of Copula Specifications in Multivariate Density Forecasts," Tinbergen Institute Discussion Papers 08-105/4, Tinbergen Institute.
- Diks, C.G.H. & Dijk, D. van & Panchenko, V., 2008. "Out-of-sample comparison of copula specifications in multivariate density forecasts," CeNDEF Working Papers 08-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-01-17 (All new papers)
- NEP-ECM-2009-01-17 (Econometrics)
- NEP-FOR-2009-01-17 (Forecasting)
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