Tail Dependence Measure for Examining Financial Extreme Co-movements

Article English OPEN
Asimit, A.V. ; Gerrard, R. J. G. ; Yanxi, H. ; Peng, L. (2016)

Modeling and forecasting extreme co-movements in financial market is important for conducting stress test in risk management. Asymptotic independence and asymptotic dependence behave drastically different in modeling such co-movements. For example, the impact of extreme events is usually overestimated whenever asymptotic dependence is wrongly assumed. On the other hand, the impact is seriously underestimated whenever the data is misspecified as asymptotic independent. Therefore, distinguishing between asymptotic independence/dependence scenarios is very informative for any decision-making and especially in risk management. We investigate the properties of the limiting conditional Kendall’s tau which can be used to detect the presence of asymptotic independence/dependence. We also propose nonparametric estimation for this new measure and derive its asymptotic limit. A simulation study shows good performances of the new measure and its combination with the coefficient of tail dependence proposed by Ledford and Tawn (1996, 1997). Finally, applications to financial and insurance data are provided.
  • References (37)
    37 references, page 1 of 4

    [1] Aragones, J.R., Blanco, C. and Dowd, K. (2001). Incorporating stress tests into market risk modeling. Derivatives Quarterly, Spring 2001, 44--49.

    [2] Asimit, A.V., Jones, B.L. (2007). Extreme behavior of bivariate elliptical distributions. Insurance: Mathematics and Economics 41(1), 53{61.

    [3] Basel Committee on Banking Supervision (2010). Basel III: a global regulatory framework for more resilient banks and banking systems , available at http://www.bis.org/publ/bcbs189.pdf

    [4] Basrak, B., Davis, R.A., and Mikosch, T. (2002). Regular variation of GARCH processes. Stoch. Process. Appl. 99, 95{115.

    [5] Breymann, W., Dias, A. and Embrechts, P. (2003). Dependence structures for multivariate high frequency data in nance. Quantative Finance 3(1), 1{14.

    [6] Cheng, S. and Peng, L. (2001). Con dence intervals for the tail index. Bernoulli 7, 751{760.

    [7] De Haan, L. and Ferreira, A. (2006). Extreme Value Theory: An Introduction. Springer-Verlag, New York.

    [8] De Haan, L. and Resnick, S.I. (1979). Derivatives of regularly varying functions in ℜd and domains of attraction of stable distributions. Stochastic Processes and their Applications 8(3), 349{355.

    [9] Doksum, K. and Samarov, A. (1995). Nonparametric estimation of global functions and a measure of the explanatory power of covariates in regressions. Ann. Statist. 23, 1443{1473.

    [10] Draisma, G., Drees, H., Ferreira, A. and de Haan, L. (2004). Bivariate tail estimation: dependence in asymptotic independence. Bernoulli 10, 251{280.

  • Metrics
    views in OpenAIRE
    views in local repository
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    City Research Online - IRUS-UK 0 30
Share - Bookmark