Statistics for tail processes of Markov chains

Verfasser / Beitragende:
[Holger Drees, Johan Segers, Michał Warchoł]
Ort, Verlag, Jahr:
2015
Enthalten in:
Extremes, 18/3(2015-09-01), 369-402
Format:
Artikel (online)
ID: 605463816
LEADER caa a22 4500
001 605463816
003 CHVBK
005 20210128100255.0
007 cr unu---uuuuu
008 210128e20150901xx s 000 0 eng
024 7 0 |a 10.1007/s10687-015-0217-1  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s10687-015-0217-1 
245 0 0 |a Statistics for tail processes of Markov chains  |h [Elektronische Daten]  |c [Holger Drees, Johan Segers, Michał Warchoł] 
520 3 |a At high levels, the asymptotic distribution of a stationary, regularly varying Markov chain is conveniently given by its tail process. The latter takes the form of a geometric random walk, the increment distribution depending on the sign of the process at the current state and on the flow of time, either forward or backward. Estimation of the tail process provides a nonparametric approach to analyze extreme values. A duality between the distributions of the forward and backward increments provides additional information that can be exploited in the construction of more efficient estimators. The large-sample distribution of such estimators is derived via empirical process theory for cluster functionals. Their finite-sample performance is evaluated via Monte Carlo simulations involving copula-based Markov models and solutions to stochastic recurrence equations. The estimators are applied to stock price data to study the absence or presence of symmetries in the succession of large gains and losses. 
540 |a Springer Science+Business Media New York, 2015 
690 7 |a Heavy-tailed Markov chains  |2 nationallicence 
690 7 |a Regular variation  |2 nationallicence 
690 7 |a Stationary time series  |2 nationallicence 
690 7 |a Tail process  |2 nationallicence 
690 7 |a Time reversibility  |2 nationallicence 
700 1 |a Drees  |D Holger  |u Department of Mathematics, University of Hamburg, Bundesstraße 55, 20146, Hamburg, Germany  |4 aut 
700 1 |a Segers  |D Johan  |u Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Voie du Roman Pays 20, B-1348, Louvain-la-Neuve, Belgium  |4 aut 
700 1 |a Warchoł  |D Michał  |u Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Voie du Roman Pays 20, B-1348, Louvain-la-Neuve, Belgium  |4 aut 
773 0 |t Extremes  |d Springer US; http://www.springer-ny.com  |g 18/3(2015-09-01), 369-402  |x 1386-1999  |q 18:3<369  |1 2015  |2 18  |o 10687 
856 4 0 |u https://doi.org/10.1007/s10687-015-0217-1  |q text/html  |z Onlinezugriff via DOI 
898 |a BK010053  |b XK010053  |c XK010000 
900 7 |a Metadata rights reserved  |b Springer special CC-BY-NC licence  |2 nationallicence 
908 |D 1  |a research-article  |2 jats 
949 |B NATIONALLICENCE  |F NATIONALLICENCE  |b NL-springer 
950 |B NATIONALLICENCE  |P 856  |E 40  |u https://doi.org/10.1007/s10687-015-0217-1  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Drees  |D Holger  |u Department of Mathematics, University of Hamburg, Bundesstraße 55, 20146, Hamburg, Germany  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Segers  |D Johan  |u Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Voie du Roman Pays 20, B-1348, Louvain-la-Neuve, Belgium  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Warchoł  |D Michał  |u Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Voie du Roman Pays 20, B-1348, Louvain-la-Neuve, Belgium  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t Extremes  |d Springer US; http://www.springer-ny.com  |g 18/3(2015-09-01), 369-402  |x 1386-1999  |q 18:3<369  |1 2015  |2 18  |o 10687