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Estimating the upcrossings index

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Abstract

For stationary sequences, under general dependence restrictions, any limiting point process for time normalized upcrossings of high levels is a compound Poisson process, i.e., there is a clustering of high upcrossings, where the underlying Poisson points represent cluster positions and the multiplicities correspond to cluster sizes. For such classes of stationary sequences, there exists the upcrossings index η, 0≤η≤1, which is directly related to the extremal index θ, 0≤θ≤1, for suitable high levels. In this paper, we consider the problem of estimating the upcrossings index η for a class of stationary sequences satisfying a mild oscillation restriction. For the proposed estimator, properties such as consistency and asymptotic normality are studied. Finally, the performance of the estimator is assessed through simulation studies for autoregressive processes and case studies in the fields of environment and finance. Comparisons with other estimators derived from well known estimators of the extremal index are also presented.

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References

  • Carlstein E (1986) The use of subseries values for estimating the variance of a general statistic from a stationary sequence. Ann Stat 14:1171–1179

    Article  MathSciNet  MATH  Google Scholar 

  • Chernick M, Hsing T, McCormick W (1991) Calculating the extremal index for a class of stationary sequences. Adv Appl Probab 23:835–850

    Article  MathSciNet  MATH  Google Scholar 

  • Drees H (2011) Bias correction for estimators of the extremal index. arXiv:1107.0935v1

  • Ferreira H (2006) The upcrossing index and the extremal index. J Appl Probab 43:927–937

    Article  MathSciNet  MATH  Google Scholar 

  • Ferreira H (2007) Runs of high values and the upcrossings index for a stationary sequence. In: Proceedings of the 56th session of the ISI

    Google Scholar 

  • Ferreira M, Ferreira H (2012) On extremal dependence: some contributions. Test 21(3):566–583

    Article  MathSciNet  MATH  Google Scholar 

  • Ferro CAT, Segers J (2002) Automatic declustering of extreme values via an estimator for the extremal index. Technical Report 2002-025. EURANDOM Eindhoven http://alexandria.tue.nl/repository/books/557750.pdf

  • Ferro CAT, Segers J (2003) Inference for clusters of extreme values. J R Stat Soc B 65:545–556

    Article  MathSciNet  MATH  Google Scholar 

  • Gomes MI, Oliveira O (2001) The bootstrap methodology in statistics of extremes—choice of the optimal sample fraction. Extremes 4(4):331–358

    Article  MathSciNet  MATH  Google Scholar 

  • Gomes MI, Hall A, Miranda C (2008) Subsampling techniques and the jackknife methodology in the estimation of the extremal index. J Stat Comput Simul 52:2022–2041

    MathSciNet  MATH  Google Scholar 

  • Hsing T (1991) Estimating the parameters of rare events. Stoch Process Appl 37:117–139

    Article  MathSciNet  MATH  Google Scholar 

  • Hsing T (1993) Extremal index estimation for a weakly dependent stationary sequence. Ann Stat 21:2043–2071

    Article  MathSciNet  MATH  Google Scholar 

  • Hsing T, Hüsler J, Leadbetter MR (1988) On the exceedance point process for a stationary sequence. Probab Theory Relat Fields 78:97–112

    Article  MATH  Google Scholar 

  • Klar B, Lindner F, Meintanis SG (2011) Specification tests for the error distribution in Garch models. Comput Stat Data Anal. doi:10.1016/j.csda.2010.05.029

    MATH  Google Scholar 

  • Künsch H (1989) The jackknife and the bootstrap for general stationary observations. Ann Math 17(3):1217–1241

    MATH  Google Scholar 

  • Lahiri SN (2003) Resampling methods for dependent data. Springer, Berlin

    Book  MATH  Google Scholar 

  • Laurini F, Tawn J (2006) The extremal index for GARCH(1, 1) processes with t-distributed innovations. http://www.scientificcommons.org/17368349

  • Leadbetter MR (1983) Extremes and local dependence in stationary processes. Z Wahrscheinlichkeitstheor Verw Geb 65:291–306

    Article  MathSciNet  MATH  Google Scholar 

  • Leadbetter MR, Nandagopalan S (1988) On exceedance point process for stationary sequences under mild oscillation restrictions. In: Hüsler J, Reiss D (eds) Extreme value theory: proceedings, Oberwolfach 1987. Springer, New York, pp 69–80

    Google Scholar 

  • Liu RY, Singh K (1992) Moving blocks jackknife and bootstrap capture weak dependence. In: Lepage R, Billard L (eds) Exploring the limits of bootstrap. Wiley, New York, pp 225–248

    Google Scholar 

  • Mikosch T, Stărică C (2000) Limit theory for the sample autocorrelations and extremes of a GARCH(1, 1) process. Ann Stat 28:1427–1451

    Article  MATH  Google Scholar 

  • Nandagopalan S (1990) Multivariate extremes and estimation of the extremal index. Ph.D. Thesis, University of North Carolina, Chapel Hill

  • Politis DN, Romano JP (1992) A circular block resampling procedure for stationary data. In: Lepage R, Billard L (eds) Exploring the limits of bootstrap. Wiley, New York, pp 263–270

    Google Scholar 

  • Politis DN, Romano JP (1994) The stationary bootstrap. J Am Stat Assoc 89:1303–1313

    Article  MathSciNet  MATH  Google Scholar 

  • Reiss R-D, Thomas M (2007) Statistical analysis of extreme values with applications to insurance, finance, hydrology and other fields. Birkhäuser, Basel

    MATH  Google Scholar 

  • Robert CY (2009) Inference for the limiting cluster size distribution of extreme values. Ann Stat 37:271–310

    Article  MATH  Google Scholar 

  • Robert CY, Segers J, Ferro C (2009) A sliding blocks estimator for the extremal index. Electron J Stat 3:993–1020

    Article  MathSciNet  MATH  Google Scholar 

  • Sebastião J, Martins AP, Pereira L, Ferreira H (2010) Clustering of upcrossings of high values. J Stat Plan Inference 140:1003–1012

    Article  MATH  Google Scholar 

  • Singh K (1981) On the asymptotic accuracy of the Efron’s bootstrap. Ann Stat 9:345–362

    Google Scholar 

  • Süveges M (2007) Likelihood estimation of the extremal index. Extremes 10:41–55

    Article  MathSciNet  MATH  Google Scholar 

  • Süveges M, Davison A (2010) Model misspecification in peaks over threshold analysis. Ann Appl Stat 4:203–221

    Article  MathSciNet  MATH  Google Scholar 

  • Weissman I, Novak SY (1998) On blocks and runs estimators of the extremal index. J Stat Plan Inference 66(2):281–288

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

We are grateful to the anonymous referees for their valuable criticisms, corrections and suggestions which helped considerably the final form of this paper.

We acknowledge the support from research unit “Centro de Matemática” of the University of Beira Interior, the research project PTDC/MAT/108575/2008 through the Foundation for Science and Technology (FCT) co-financed by FEDER/COMPETE and SFRH/BD/41439/2007.

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Correspondence to A. P. Martins.

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Sebastião, J.R., Martins, A.P., Ferreira, H. et al. Estimating the upcrossings index. TEST 22, 549–579 (2013). https://doi.org/10.1007/s11749-013-0315-9

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