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Out-of-sample comparison of copula specifications in multivariate density forecasts

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  • Diks, Cees
  • Panchenko, Valentyn
  • van Dijk, Dick

Abstract

We 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 on the competing copulas: in particular it allows for parameter estimation uncertainty and for the copulas to be nested or non-nested. 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-t copula is favored over Gaussian, Gumbel and Clayton copulas.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 34 (2010)
Issue (Month): 9 (September)
Pages: 1596-1609

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Handle: RePEc:eee:dyncon:v:34:y:2010:i:9:p:1596-1609

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Web page: http://www.elsevier.com/locate/jedc

Related research

Keywords: Copula-based density forecast Empirical copula Kullback-Leibler information criterion Out-of-sample forecast evaluation Semi-parametric statistics;

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References

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  1. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
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Citations

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Cited by:
  1. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 90-110.
  2. Krenar Avdulaj & Jozef Barunik, 2013. "Can we still benefit from international diversification? The case of the Czech and German stock markets," Papers 1308.6120, arXiv.org, revised Sep 2013.
  3. Jean-David Fermanian, 2012. "An overview of the goodness-of-fit test problem for copulas," Papers 1211.4416, arXiv.org.
  4. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Series Working Papers 533, University of Oxford, Department of Economics.
  5. Weiß, Gregor N.F., 2011. "Are Copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 173-188, May.
  6. Marc Gronwald & Janina Ketterer & Stefan Trück, 2011. "The Dependence Structure between Carbon Emission Allowances and Financial Markets - A Copula Analysis," CESifo Working Paper Series 3418, CESifo Group Munich.
  7. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
  8. Hayette Gatfaoui, 2010. "Investigating the dependence structure between credit default swap spreads and the U.S. financial market," Annals of Finance, Springer, vol. 6(4), pages 511-535, October.

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