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Multiple break detection in the correlation structure of random variables

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  • Galeano, Pedro
  • Wied, Dominik

Abstract

Correlations between random variables play an important role in applications, e.g. in financial analysis. More precisely, accurate estimates of the correlation between financial returns are crucial in portfolio management. In particular, in periods of financial crisis, extreme movements in asset prices are found to be more highly correlated than small movements. It is precisely under these conditions that investors are extremely concerned about changes on correlations. A binary segmentation procedure to detect the number and position of multiple change points in the correlation structure of random variables is proposed. The procedure assumes that expectations and variances are constant and that there are sudden shifts in the correlations. It is shown analytically that the proposed algorithm asymptotically gives the correct number of change points and the change points are consistently estimated. It is also shown by simulation studies and by an empirical application that the algorithm yields reasonable results.

Suggested Citation

  • Galeano, Pedro & Wied, Dominik, 2014. "Multiple break detection in the correlation structure of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 262-282.
    • Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:262-282
    DOI: 10.1016/j.csda.2013.02.031
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    References listed on IDEAS

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    Cited by:

    1. Josua Gösmann & Daniel Ziggel, 2018. "An innovative risk management methodology for trading equity indices based on change points," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 99-109, March.
    2. Dominik Wied & Daniel Ziggel & Tobias Berens, 2013. "On the application of new tests for structural changes on global minimum-variance portfolios," Statistical Papers, Springer, vol. 54(4), pages 955-975, November.
    3. Bastidon, Cécile & Parent, Antoine & Jensen, Pablo & Abry, Patrice & Borgnat, Pierre, 2020. "Graph-based era segmentation of international financial integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    4. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    5. Adams, Zeno & Glück, Thorsten, 2013. "Financialization in Commodity Markets: Disentangling the Crisis from the Style Effect," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79949, Verein für Socialpolitik / German Economic Association.
    6. repec:hal:spmain:info:hdl:2441/ps168627s85g86i5u1aj5akpm is not listed on IDEAS
    7. Duan, Fang, 2022. "Forecasting risk measures based on structural breaks in the correlation matrix," Ruhr Economic Papers 945, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    8. Choi, Ji-Eun & Shin, Dong Wan, 2019. "Moving block bootstrapping for a CUSUM test for correlation change," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 95-106.
    9. Bastidon, Cécile & Parent, Antoine & Jensen, Pablo & Abry, Patrice & Borgnat, Pierre, 2020. "Graph-based era segmentation of international financial integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    10. Bampinas, Georgios & Panagiotidis, Theodore & Politsidis, Panagiotis N., 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Journal of International Money and Finance, Elsevier, vol. 137(C).
    11. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    12. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.
    13. Adams, Zeno & Füss, Roland & Glück, Thorsten, 2017. "Are correlations constant? Empirical and theoretical results on popular correlation models in finance," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 9-24.
    14. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    15. Adams, Zeno & Glück, Thorsten, 2015. "Financialization in commodity markets: A passing trend or the new normal?," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 93-111.
    16. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    17. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2013. "Über die Anwendbarkeit eines neuen Fluktuationstests für Korrelationen auf Finanzzeitreihen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 87-103, March.
    18. David Hallac & Peter Nystrup & Stephen Boyd, 2019. "Greedy Gaussian segmentation of multivariate time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 727-751, September.
    19. Sylvia Gottschalk, 2023. "From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2843-2873, July.
    20. Choi, Ji-Eun & Shin, Dong Wan, 2020. "A self-normalization test for correlation change," Economics Letters, Elsevier, vol. 193(C).
    21. Nguyen, Quynh Nga & Aboura, Sofiane & Chevallier, Julien & Zhang, Lyuyuan & Zhu, Bangzhu, 2020. "Local Gaussian correlations in financial and commodity markets," European Journal of Operational Research, Elsevier, vol. 285(1), pages 306-323.
    22. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises: A local Gaussian correlation approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(12), pages 1179-1204, December.
    23. Ordu-Akkaya, Beyza Mina & Soytas, Ugur, 2020. "Unconventional monetary policy and financialization of commodities," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    24. Adams, Zeno & Glueck, Thorsten, 2014. "Financialization in Commodity Markets: A Passing Trend or the New Normal?," Working Papers on Finance 1413, University of St. Gallen, School of Finance, revised Aug 2015.
    25. Bücher, Axel & Jäschke, Stefan & Wied, Dominik, 2015. "Nonparametric tests for constant tail dependence with an application to energy and finance," Journal of Econometrics, Elsevier, vol. 187(1), pages 154-168.

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