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Extreme value theory for space-time processes with heavy-tailed distributions

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  • Davis, Richard A.
  • Mikosch, Thomas

Abstract

Many real-life time series exhibit clusters of outlying observations that cannot be adequately modeled by a Gaussian distribution. Heavy-tailed distributions such as the Pareto distribution have proved useful in modeling a wide range of bursty phenomena that occur in areas as diverse as finance, insurance, telecommunications, meteorology, and hydrology. Regular variation provides a convenient and unified background for studying multivariate extremes when heavy tails are present. In this paper, we study the extreme value behavior of the space-time process given by where is an iid sequence of random fields on [0,1]d with values in the Skorokhod space of càdlàg functions on [0,1]d equipped with the J1-topology. The coefficients [psi]i are deterministic real-valued fields on . The indices and t refer to the observation of the process at location and time t. For example, , could represent the time series of annual maxima of ozone levels at location . The problem of interest is determining the probability that the maximum ozone level over the entire region [0,1]2 does not exceed a given standard level in n years. By establishing a limit theory for point processes based on , t=1,...,n, we are able to provide approximations for probabilities of extremal events. This theory builds on earlier results of de Haan and Lin [L. de Haan, T. Lin, On convergence toward an extreme value distribution in , Ann. Probab. 29 (2001) 467-483] and Hult and Lindskog [H. Hult, F. Lindskog, Extremal behavior of regularly varying stochastic processes, Stochastic Process. Appl. 115 (2) (2005) 249-274] for regular variation on and Davis and Resnick [R.A. Davis, S.I. Resnick, Limit theory for moving averages of random variables with regularly varying tail probabilities, Ann. Probab. 13 (1985) 179-195] for extremes of linear processes with heavy-tailed noise.

Suggested Citation

  • Davis, Richard A. & Mikosch, Thomas, 2008. "Extreme value theory for space-time processes with heavy-tailed distributions," Stochastic Processes and their Applications, Elsevier, vol. 118(4), pages 560-584, April.
  • Handle: RePEc:eee:spapps:v:118:y:2008:i:4:p:560-584
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    References listed on IDEAS

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    1. Basrak, Bojan & Davis, Richard A. & Mikosch, Thomas, 2002. "Regular variation of GARCH processes," Stochastic Processes and their Applications, Elsevier, vol. 99(1), pages 95-115, May.
    2. Hult, Henrik & Lindskog, Filip, 2005. "Extremal behavior of regularly varying stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 115(2), pages 249-274, February.
    3. P. Brockwell, 2001. "Lévy-Driven Carma Processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 113-124, March.
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    Cited by:

    1. Opitz, T., 2013. "Extremal t processes: Elliptical domain of attraction and a spectral representation," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 409-413.
    2. Wu, Lifan & Samorodnitsky, Gennady, 2020. "Regularly varying random fields," Stochastic Processes and their Applications, Elsevier, vol. 130(7), pages 4470-4492.
    3. Ho, Zhen Wai Olivier & Dombry, Clément, 2019. "Simple models for multivariate regular variation and the Hüsler–Reiß Pareto distribution," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 525-550.
    4. Kabluchko, Zakhar, 2009. "Extremes of space-time Gaussian processes," Stochastic Processes and their Applications, Elsevier, vol. 119(11), pages 3962-3980, November.
    5. Davis, Richard A. & Mikosch, Thomas & Zhao, Yuwei, 2013. "Measures of serial extremal dependence and their estimation," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2575-2602.
    6. M. Ghil & Pascal Yiou & Stéphane Hallegatte & B. D. Malamud & P. Naveau & A. Soloviev & P. Friederichs & V. Keilis-Borok & D. Kondrashov & V. Kossobokov & O. Mestre & C. Nicolis & H. W. Rust & P. Sheb, 2011. "Extreme events: dynamics, statistics and prediction," Post-Print hal-00716514, HAL.
    7. Constantinescu, Corina & Hashorva, Enkelejd & Ji, Lanpeng, 2011. "Archimedean copulas in finite and infinite dimensions—with application to ruin problems," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 487-495.
    8. Clémençon, Stephan & Huet, Nathan & Sabourin, Anne, 2024. "Regular variation in Hilbert spaces and principal component analysis for functional extremes," Stochastic Processes and their Applications, Elsevier, vol. 174(C).
    9. Davydov, Youri & Dombry, Clément, 2012. "On the convergence of LePage series in Skorokhod space," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 145-150.
    10. Richard A. Davis & Claudia Klüppelberg & Christina Steinkohl, 2013. "Statistical inference for max-stable processes in space and time," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(5), pages 791-819, November.
    11. José G. Gómez-García & Christophe Chesneau, 2021. "A Dependent Lindeberg Central Limit Theorem for Cluster Functionals on Stationary Random Fields," Mathematics, MDPI, vol. 9(3), pages 1-14, January.
    12. Robert, Christian Y., 2022. "Testing for changes in the tail behavior of Brown–Resnick Pareto processes," Stochastic Processes and their Applications, Elsevier, vol. 144(C), pages 312-368.

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