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Multiple break detection in the correlation structure of random variables

Listed author(s):
  • Galeano, Pedro
  • Wied, Dominik

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.

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File URL: http://www.sciencedirect.com/science/article/pii/S016794731300087X
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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 76 (2014)
Issue (Month): C ()
Pages: 262-282

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Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:262-282
DOI: 10.1016/j.csda.2013.02.031
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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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.
  2. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
  3. Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
  4. Pedro Galeano & Ruey S. Tsay, 2010. "Shifts in Individual Parameters of a GARCH Model," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(1), pages 122-153, Winter.
  5. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
  6. De Gooijer, Jan G., 2006. "Detecting change-points in multidimensional stochastic processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1892-1903, December.
  7. Krishnan, C.N.V. & Petkova, Ralitsa & Ritchken, Peter, 2009. "Correlation risk," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 353-367, June.
  8. Elena Andreou & Eric Ghysels, 2002. "Detecting multiple breaks in financial market volatility dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 579-600.
  9. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
  10. Bai, Jushan, 1997. "Estimating Multiple Breaks One at a Time," Econometric Theory, Cambridge University Press, vol. 13(03), pages 315-352, June.
  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.
  13. Ploberger, Werner & Kramer, Walter & Kontrus, Karl, 1989. "A new test for structural stability in the linear regression model," Journal of Econometrics, Elsevier, vol. 40(2), pages 307-318, February.
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