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arXiv · 1604.00954

Inference on the tail process with application to financial time series modelling

R. A. Davis·H. Drees·J. Segers·M. Warchoł
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Abstract

To draw inference on serial extremal dependence within heavy-tailed Markov chains, Drees, Segers and Warcho{\l} [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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R. A. Davis, H. Drees, J. Segers, M. Warchoł. 2016-04-04. Inference on the tail process with application to financial time series modelling. https://arxiv.org/abs/1604.00954

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