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

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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Document type	Document de travail (Working Paper)
Access type	Accès libre
Publication date	2018
Language	Anglais
Number of pages	22 pages
Collection	ISBA Discussion Paper - 2018/02
Affiliation	UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Keywords	Financial time series ; Heavy-tails ; Multiplier block bootstrap ; Regular variation ; Shock persistence ; Stationary time series ; Tail process
Links	http://hdl.handle.net/2078.1/195211[Handle]

See also
	[boreal:198233]	Inference on the tail process with application to financial time series modelling