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When is a copula constant? A test for changing relationships

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  • Busetti, F.
  • Harvey, A.

Abstract

A copula defines the probability that observations from two time series lie below given quantiles. It is proposed that stationarity tests constructed from indicator variables be used to test against the hypothesis that the copula is changing over time. Tests associated with different quantiles may point to changes in different parts of the copula, with the lower quantiles being of particular interest in financial applications concerned with risk. Tests located at the median provide an overall test of a changing relationship. The properties of various tests are compared and it is shown that they are still effective if pre-filtering is carried out to correct for changing volatility or, more generally, changing quantiles. Applying the tests to daily stock return indices in Korea and Thailand over the period 1995-9 indicates that the relationship between them is not constant over time.

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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0841.

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Date of creation: Aug 2008
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Handle: RePEc:cam:camdae:0841

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Keywords: Concordance; quantile; rank correlation; stationarity test; tail dependence.;

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References

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  3. Busettti, F. & Harvey, A., 2007. "Tests of time-invariance," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 0701, Faculty of Economics, University of Cambridge.
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  6. Andrew Patton, 2004. "Modelling Asymmetric Exchange Rate Dependence," Working Papers, Warwick Business School, Finance Group wp04-04, Warwick Business School, Finance Group.
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  15. Sanjiv Ranjan Das & Raman Uppal, 2004. "Systemic Risk and International Portfolio Choice," Journal of Finance, American Finance Association, American Finance Association, vol. 59(6), pages 2809-2834, December.
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Citations

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Cited by:
  1. Wang, Yi-Chiuan & Wu, Jyh-Lin & Lai, Yi-Hao, 2013. "A revisit to the dependence structure between the stock and foreign exchange markets: A dependence-switching copula approach," Journal of Banking & Finance, Elsevier, Elsevier, vol. 37(5), pages 1706-1719.
  2. Chollete, Loran & Pena, Victor de la & Lu, Ching-Chih, 2009. "International Diversification: A Copula Approach," UiS Working Papers in Economics and Finance 2009/27, University of Stavanger.
  3. Harvey, Andrew, 2010. "Tracking a changing copula," Journal of Empirical Finance, Elsevier, Elsevier, vol. 17(3), pages 485-500, June.
  4. Liu, Xiaochun, 2011. "Modeling the time-varying skewness via decomposition for out-of-sample forecast," MPRA Paper 41248, University Library of Munich, Germany.
  5. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 723-736.
  6. S Zhang & I Paya & D Peel, 2009. "Linkages between Shanghai and Hong Kong stock indices," Working Papers 599248, Lancaster University Management School, Economics Department.
  7. Chollete, Loran & Ning, Cathy, 2009. "The Dependence Structure of Macroeconomic Variables in the US," UiS Working Papers in Economics and Finance 2009/31, University of Stavanger.
  8. Fabio Busetti, 2012. "On detecting end-of-sample instabilities," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area 881, Bank of Italy, Economic Research and International Relations Area.
  9. Thanaset Chevapatrakul & Kai-Hong Tee, 2014. "The Effects of News Events on Market Contagion: Evidence from the 2007-2009 Financial Crisis," Discussion Papers 2014/08, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
  10. Krämer, Walter & van Kampen, Maarten, 2011. "A simple nonparametric test for structural change in joint tail probabilities," Economics Letters, Elsevier, Elsevier, vol. 110(3), pages 245-247, March.

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