ArticleOriginal scientific text

Title

Estimating the extremal index through the tail dependence concept

Authors 1

Affiliations

  1. Center of Mathematics, University of Minho, Portugal

Abstract

The extremal index Θ is an important parameter in extreme value analysis when extending results from independent and identically distributed sequences to stationary ones. A connection between the extremal index and the tail dependence coefficient allows the introduction of new estimators. The proposed ones are easy to compute and we analyze their performance through a simulation study. Comparisons with other existing methods are also presented. Case studies within environment are considered in the end.

Keywords

extreme value theory, extremal index, tail dependence coefficient

Bibliography

  1. M.A. Ancona-Navarrete and J.A. Tawn, A comparison of methods for estimating the extremal index, Extremes 3 (2000), 5-38.
  2. J. Beirlant, Y. Goegebeur, J. Segers and J. Teugels, Statistics of Extremes: Theory and Application 9John Wiley, 2004).
  3. P. Capéraà, A.L. Fougères and C. Genest, A nonparametric estimation procedure for bivariate extreme value copulas, Biometrika 84 (1997), 567-577.
  4. M.R. Chernick, T. Hsing and W.P. McCormick, Calculating the extremal index for a class of stationary sequences, Adv. Appl. Probab. 23 (1991), 835-850.
  5. S.G. Coles, An Introduction to Statistical Modelling of Extreme Values (London, Springer, 2001).
  6. M. Ferreira, Nonparametric estimation of the tail dependence coefficient, REVSTAT 11 (2013), 1-16.
  7. M. Ferreira and H. Ferreira, On extremal dependence: some contributions, TEST 21 (2012a), 566-583.
  8. H. Ferreira and M. Ferreira, On extremal dependence of block vectors, Kybernetika 48 (2012b), 988-1006.
  9. C.A. Ferro and J. Segers, Inference for clusters of extremes, J.R. Stat. Soc. Ser. B Stat. Methodol. 65 (2003), 545-556.
  10. G. Frahm, M. Junker and R. Schmidt, Estimating the tail-dependence coefficient: properties and pitfalls, Insurance Math. Econom. 37 (2005), 80-100.
  11. C. Genest and J. Segers J., Rank-based inference for bivariate extreme-value copulas, Ann. Statist. 37 (2009), 2990-3022.
  12. M.I. Gomes, A. Hall and C. Miranda, Subsampling techniques and the jackknife methodology in the estimation of the extremal index, J. Stat. Comput. Simul. 52 (2008), 2022-2041.
  13. T. Hsing, J. Husler and M.R. Leadbetter, On the exceedance point process for a stationary sequence, Probab. Theory Related Fields 78 (1988), 97-112.
  14. M.R. Leadbetter, Extremes and local dependence in stationary sequences, Z. Wahrsch. Ver. Geb. 65 (1983), 291-306.
  15. R.M. Loynes, Extreme Values in Uniformly Mixing Stationary Stochastic Processes, Annals of Mathematical Statistics 36 (1965), 993-999.
  16. S. Nandagopalan, Multivariate extremes and estimation of the extremal index (Ph.D. Thesis, University of North Carolina at Chapel Hill, 1990).
  17. G.L. O'Brien, The maximum term of uniformly mixing stationary sequences, Z. Wahrsch. Ver. Geb. 30 (1974), 57-63.
  18. R.D. Reiss, M. Thomas, Statistical analysis of extreme values with applications to insurance, finance, hydrology and other fields (Birkhäuser, Basel, 2007).
  19. R. Schmidt and U. Stadtmüller, Nonparametric estimation of tail dependence, Scandinavian J. Statist. 33 (2006), 307-335.
  20. J.R. Sebastiăo, A.P. Martins, H. Ferreira and L.Pereira, Estimating the upcrossings index, TEST 22 (2013), 549-579.
  21. M. Sibuya, Bivariate extreme statistics, Ann. Inst. Stat. Math. 11 (1960), 195-210.
  22. M. Süveges, Likelihood estimation of the extremal index, Extremes 10 (2007), 41-55.
Pages:
61-74
Main language of publication
English
Received
2015-04-01
Published
2015
Exact and natural sciences