The tail empirical process for long memory stochastic volatility sequences

DOI10.1016/j.spa.2010.09.001zbMath1253.60030arXiv1001.2916OpenAlexW1556455486MaRDI QIDQ617913

Rafał Kulik, Philippe Soulier

Publication date: 14 January 2011

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1001.2916

zbMATH Keywords

stochastic volatilitylong memorytail empirical processhill estimatortail empirical distribution function

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05) Order statistics; empirical distribution functions (62G30) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)

Related Items (19)

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Cites Work

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• Extreme quantile estimation for dependent data, with applications to finance
• Empirical process of long-range dependent sequences when parameters are estimated
• Weak convergence of the tail empirical process for dependent sequences
• Sums of extreme values of subordinated long-range dependent sequences: moving averages with finite variance
• Limit theorems for tail processes with application to intermediate quantile estimation
• Central limit theorem for the empirical process of a linear sequence with long memory
• The detection and estimation of long memory in stochastic volatility
• Limit theorems for functionals of moving averages
• Optimal rates of convergence for estimates of the extreme value index
• Weighted approximations of tail processes for \(\beta\)-mixing random variables.
• Limit theorems for nonlinear functionals of a stationary Gaussian sequence of vectors
• A note on tightness
• Stochastic-Process Limits
• Point process convergence of stochastic volatility processes with application to sample autocorrelation
• Long Memory in Nonlinear Processes
• Heavy-Tail Phenomena

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