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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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    1. Galeano, Pedro, 2007. "The use of cumulative sums for detection of changepoints in the rate parameter of a Poisson Process," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6151-6165, August.
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    8. Elena Andreou & Eric Ghysels, 2002. "Detecting multiple breaks in financial market volatility dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., pages 579-600.
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    11. Wied, Dominik & Krämer, Walter & Dehling, Herold, 2012. "Testing For A Change In Correlation At An Unknown Point In Time Using An Extended Functional Delta Method," Econometric Theory, Cambridge University Press, vol. 28(03), pages 570-589, June.
    12. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
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    Citations

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    Cited by:

    1. 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.
    2. repec:spr:testjl:v:26:y:2017:i:2:d:10.1007_s11749-016-0513-3 is not listed on IDEAS
    3. 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.
    4. 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.
    5. 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.
    6. Adams, Zeno & Glück, Thorsten, 2013. "Financialization in Commodity Markets: Disentangling the Crisis from the Style Effect," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79949, Verein für Socialpolitik / German Economic Association.
    7. repec:eee:jbfina:v:84:y:2017:i:c:p:9-24 is not listed on IDEAS
    8. 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.
    9. Adams, Zeno & Fuess, Roland & Glueck, Thorsten, 2016. "Are Correlations Constant? Empirical and Theoretical Results on Popular Correlation Models in Finance," Working Papers on Finance 1613, University of St. Gallen, School of Finance.
    10. 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.
    11. 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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