Modeling serial extremal dependence

Davis, Richard;Holger, Drees;Segers, Johan;Warchol, Michal
(2016) , 20 pages

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Authors
  • Davis, RichardColumbia University, NY, USA
    Author
  • Holger, DreesUniversity of Hamburg, Germany
    Author
  • Segers, JohanUCLouvain
    Author
  • Warchol, MichalUCLouvain
    Author
Abstract
To draw inference on serial extremal dependence within heavy-tailed Markov chains, Drees, Segers and Warchol [Extremes (2015) 18, 369-402] proposed nonparametric estimators of the spectral tail process. The methodology can be extended to the more general setting of a stationary, regularly varying time series. The large-sample distribution of the estimators is derived via empirical process theory for cluster functionals. The finite-sample performance of these estimators is evaluated via Monte Carlo simulations. Moreover, two different bootstrap schemes are employed which yield confidence intervals for the pre-asymptotic spectral tail process: the stationary bootstrap and the multiplier block bootstrap. The estimators are applied to stock price data to study the persistence of positive and negative shocks.
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Davis, R., Holger, D., Segers, J., & Warchol, M. (2016). Modeling serial extremal dependence (ISBA Discussion Paper 2016/16). https://hdl.handle.net/2078.5/268496