Detecting changes in cross-sectional dependence in multivariate time series

Journal of Multivariate Analysis

Volumn 132, Issue , 2014, Pages 111-128

  Bücher, Axel ^a   Kojadinovic, Ivan ^b   Rohmer, Tom ^b   Segers, Johan ^c  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b UNIVERSITÉ DE PAU ET DES PAYS DE L'ADOUR   (France)

^c UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

Change point detection; Empirical copula; Multiplier central limit theorem; Partial sum process; Ranks; Strong mixing

Indexed keywords

EID: 84908407614     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/j.jmva.2014.07.012     Document Type: Article

References (39)

1. Auestad B., Tjøstheim D. Identification of nonlinear time series: First order characterization and order determination. Biometrika 1990, 77:669-687.
2. Bai J. Weak convergence of the sequential empirical processes of residuals in ARMA models. Ann. Statist. 1994, 22(4):2051-2061.
3. Bücher A. A note on nonparametric estimation of bivariate tail dependence. Statist. Risk Modelling 2014, 31(2):151-162.
4. Bücher A. A note on weak convergence of the sequential multivariate empirical process under strong mixing. J. Theoret. Probab. 2014, To appear. 10.1007/s10959-013-0529-5.
5. Bücher A., Dette H. A note on bootstrap approximations for the empirical copula process. Statist. Probab. Lett. 2010, 80(23-24):1925-1932.
6. A. Bücher, I. Kojadinovic, A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing, 2013. arxiv:1306.3930.
7. Bücher A., Ruppert M. Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique. J. Multivariate Anal. 2013, 116:208-229.
8. Bücher A., Segers J. Extreme value copula estimation based on block maxima of a multivariate stationary time series. Extremes 2014, To appear. 10.1007/s10687-014-0195-8.
9. Bücher A., Volgushev S. Empirical and sequential empirical copula processes under serial dependence. J. Multivariate Anal. 2013, 119:61-70.
10. Bühlmann P. The blockwise bootstrap in time series and empirical processes 1993, (Ph.D. thesis), ETH Zürich, Diss. ETH No. 10354.
11. Csörgo M., Horváth L. Limit Theorems in Change-Point Analysis. Wiley Series in Probability and Statistics 1997, John Wiley & Sons, Chichester, UK.
12. H. Dehling, D. Vogel, M. Wendler, D. Wied, An efficient and robust test for a change-point in correlation, 2013. arxiv:1203.4871.
13. Genest C., Ghoudi K., Rivest L.-P. Discussion of "Understanding relationships using copulas", by E. Frees and E. Valdez. N. Am. Actuar. J. 1998, 3:143-149.
14. Genest C., Rémillard B., Beaudoin D. Goodness-of-fit tests for copulas: A review and a power study. Insurance Math. Econom. 2009, 44:199-213.
15. Genest C., Segers J. On the covariance of the asymptotic empirical copula process. J. Multivariate Anal. 2010, 101:1837-1845.
16. Gombay E., Horváth L. Change-points and bootstrap. Environmetrics 1999, 10(6).
17. Gombay E., Horváth L. Rates of convergence for U-statistic processes and their bootstrapped versions. J. Statist. Plann. Inference 2002, 102:247-272.
18. M. Hofert, I. Kojadinovic, M. Mächler, J. Yan, copula: Multivariate dependence with copulas, 2013. URL: . R package version 0.999-7. http://CRAN.R-project.org/package%3Dcopula.
19. Holmes M., Kojadinovic I., Quessy J.-F. Nonparametric tests for change-point detection à la Gombay and Horváth. J. Multivariate Anal. 2013, 115:16-32.
20. Inoue A. Testing for distributional change in time series. Econometric Theory 2001, 17(1):156-187.
21. Khoudraji A. Contributions à l'étude des copules et à la modélisation des valeurs extrêmes bivariées 1995, (Ph.D. thesis), Université Laval, Québec, Canada.
22. I. Kojadinovic, npcp: Some nonparametric tests for change-point detection in (multivariate) observations, 2014. URL: . R package version 0.1-0. http://CRAN.R-project.org/package%3Dnpcp.
23. Kojadinovic I., Segers J., Yan J. Large-sample tests of extreme-value dependence for multivariate copulas. Canad. J. Statist. 2011, 39(4):703-720.
24. Kojadinovic I., Yan J., Holmes M. Fast large-sample goodness-of-fit for copulas. Statist. Sinica 2011, 21(2):841-871.
25. Liebscher E. Construction of asymmetric multivariate copulas. J. Multivariate Anal. 2008, 99:2234-2250.
26. Paparoditis E., Politis D.N. Tapered block bootstrap. Biometrika 2001, 88(4):1105-1119.
27. B. Pfaff, A. McNeil, QRM: provides R-language code to examine quantitative risk management concepts, 2013. URL: . R package version 0.4-9. http://CRAN.R-project.org/package%3DQRM.
28. Quessy J.-F., Saïd M., Favre A.-C. Multivariate Kendall's tau for change-point detection in copulas. Canad. J. Statist. 2013, 41:65-82.
29. R Development Core Team R: A Language and Environment for Statistical Computing 2013, R Foundation for Statistical Computing, Vienna, Austria, URL: http://www.R-project.org.
30. Rémillard B. Goodness-of-fit tests for copulas of multivariate time series. Soc. Sci. Res. Netw., 1729982 2010, 1-32. URL: http://ssrn.com/abstract=1729982.
31. Rémillard B., Scaillet O. Testing for equality between two copulas. J. Multivariate Anal. 2009, 100(3):377-386.
32. Romano J.P., Wolf M. A more general central limit theorem for m-dependent random variables with unbounded m. Statist. Probab. Lett. 2000, 47:115-124.
33. Scaillet O. A Kolmogorov-Smirnov type test for positive quadrant dependence. Canad. J. Statist. 2005, 33:415-427.
34. Segers J. Asymptotics of empirical copula processes under nonrestrictive smoothness assumptions. Bernoulli 2012, 18:764-782.
35. Sklar A. Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 1959, 8:229-231.
36. A. Trapletti, K. Hornik, tseries: Time series analysis and computational finance, 2013. URL: . R package version 0.10-32. http://CRAN.R-project.org/package%3Dtseries.
37. van der Vaart A.W., Wellner J.A. Weak Convergence and Empirical Processes 2000, Springer, New York. second ed.
38. M. van Kampen, D. Wied, A nonparametric constancy test for copulas under mixing conditions, Working Paper, 2013. URL: . http://www.statistik.tu-dortmund.de/vankampen-eng.html.
39. Wied D., Dehling H., van Kampen M., Vogel D. A fluctuation test for constant spearman's rho with nuisance-free limit distribution. Comput. Statist. Data Anal. 2014, 76:723-736.