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Investigating Extreme Dependences: Concepts and Tools

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  • Y. Malevergne

    (Univ. Nice and Univ. Lyon I)

  • D. Sornette

    (CNRS and Univ. Nice and UCLA)

Abstract

We investigate the relative information content of six measures of dependence between two random variables $X$ and $Y$ for large or extreme events for several models of interest for financial time series. The six measures of dependence are respectively the linear correlation $\rho^+_v$ and Spearman's rho $\rho_s(v)$ conditioned on signed exceedance of one variable above the threshold $v$, or on both variables ($\rho_u$), the linear correlation $\rho^s_v$ conditioned on absolute value exceedance (or large volatility) of one variable, the so-called asymptotic tail-dependence $\lambda$ and a probability-weighted tail dependence coefficient ${\bar \lambda}$. The models are the bivariate Gaussian distribution, the bivariate Student's distribution, and the factor model for various distributions of the factor. We offer explicit analytical formulas as well as numerical estimations for these six measures of dependence in the limit where $v$ and $u$ go to infinity. This provides a quantitative proof that conditioning on exceedance leads to conditional correlation coefficients that may be very different from the unconditional correlation and gives a straightforward mechanism for fluctuations or changes of correlations, based on fluctuations of volatility or changes of trends. Moreover, these various measures of dependence exhibit different and sometimes opposite behaviors, suggesting that, somewhat similarly to risks whose adequate characterization requires an extension beyond the restricted one-dimensional measure in terms of the variance (volatility) to include all higher order cumulants or more generally the knowledge of the full distribution, tail-dependence has also a multidimensional character.

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Paper provided by arXiv.org in its series Papers with number cond-mat/0203166.

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Date of creation: Mar 2002
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Publication status: Published in transformed and extended in the book ``Extreme Financial Risks (From dependence to risk management)'' (Springer, Heidelberg, 2006)
Handle: RePEc:arx:papers:cond-mat/0203166

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References

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  1. Starica, Catalin, 1999. "Multivariate extremes for models with constant conditional correlations," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 515-553, December.
  2. Brian H. Boyer & Michael S. Gibson & Mico Loretan, 1997. "Pitfalls in tests for changes in correlations," International Finance Discussion Papers 597, Board of Governors of the Federal Reserve System (U.S.).
  3. de Vries, Casper G & Hartmann, Philipp & Straetmans, Stefan, 2001. "Asset Market Linkages in Crisis Periods," CEPR Discussion Papers 2916, C.E.P.R. Discussion Papers.
  4. Y. Malevergne & D. Sornette, 2002. "Tail Dependence of Factor Models," Papers cond-mat/0202356, arXiv.org.
  5. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
  6. Y. Malevergne & D. Sornette, 2001. "Testing the Gaussian Copula Hypothesis for Financial Assets Dependences," Papers cond-mat/0111310, arXiv.org.
  7. Mervyn A. King & Sushil Wadhwani, 1989. "Transmission of Volatility Between Stock Markets," NBER Working Papers 2910, National Bureau of Economic Research, Inc.
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  9. Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Correlation structure of extreme stock returns," Papers cond-mat/0006034, arXiv.org, revised Jan 2001.
  10. Vineer Bhansali & Mark B. Wise, 2001. "Forecasting Portfolio Risk in Normal and Stressed Markets," Papers nlin/0108022, arXiv.org, revised Sep 2001.
  11. Quintos, Carmela & Fan, Zhenhong & Phillips, Peter C B, 2001. "Structural Change Tests in Tail Behaviour and the Asian Crisis," Review of Economic Studies, Wiley Blackwell, vol. 68(3), pages 633-63, July.
  12. H. A. Hauksson & M. Dacorogna & T. Domenig & U. Mller & G. Samorodnitsky, 2001. "Multivariate extremes, aggregation and risk estimation," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 79-95.
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  15. Patton, Andrew J, 2001. "Estimation of Copula Models for Time Series of Possibly Different Length," University of California at San Diego, Economics Working Paper Series qt3fc1c8hw, Department of Economics, UC San Diego.
  16. Mansilla, R., 2001. "Algorithmic complexity of real financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 483-492.
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Citations

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Cited by:
  1. Y. Malevergne & D. Sornette, 2002. "Hedging Extreme Co-Movements," Papers cond-mat/0205636, arXiv.org.
  2. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Shocks in financial markets, price expectation, and damped harmonic oscillators," Papers 1103.1992, arXiv.org, revised Sep 2011.
  3. Harry. M Kat, 2002. "The Dangers of Using Correlation to Measure Dependence," ICMA Centre Discussion Papers in Finance icma-dp2002-23, Henley Business School, Reading University.
  4. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Correlation of financial markets in times of crisis," Papers 1102.1339, arXiv.org, revised Mar 2011.

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