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

To draw inference on serial extremal dependence within heavy-tailed Markov chains, Drees et al., (2015) 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 distrib...

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Bibliographic Details
Published in:Journal of econometrics 2018-08, Vol.205 (2), p.508-525
Main Authors: Davis, Richard A., Drees, Holger, Segers, Johan, Warchoł, Michał
Format: Article
Language:English
Subjects:
Bootstrap method
Estimating techniques
Financial time series
Heavy-tails
Inference
Monte Carlo simulation
Multiplier block bootstrap
Regular variation
Shock persistence
Stationary time series
Studies
Tail process
Time series
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Summary:To draw inference on serial extremal dependence within heavy-tailed Markov chains, Drees et al., (2015) 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.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2018.01.009