Asymptotic behavior of hill's estimator for autoregressive data

DOI10.1080/15326349708807448zbMath1047.62079OpenAlexW2158372120MaRDI QIDQ4371852

Sidney I. Resnick, Cătălin Stărică

Publication date: 1997

Published in: Communications in Statistics. Stochastic Models (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/1813/9049

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistics of extreme values; tail inference (62G32)

Related Items (29)

Weighted approximations of tail processes for \(\beta\)-mixing random variables. ⋮ Asymptotic and finite sample properties of Hill-type estimators in the presence of errors in observations ⋮ Hill's estimator for the tail index of an ARMA model ⋮ On consistency of the likelihood moment estimators for a linear process with regularly varying innovations ⋮ On the foundations of multivariate heavy-tail analysis ⋮ ESTIMATION OF THE MAXIMAL MOMENT EXPONENT OF A GARCH(1,1) SEQUENCE ⋮ Test for tail index change in stationary time series with Pareto-type marginal distribution ⋮ Estimation of central shapes of error distributions in linear regression problems ⋮ Multivariate Hill Estimators ⋮ Change-Point Tests for the Tail Parameter of Long Memory Stochastic Volatility Time Series ⋮ Asymptotic properties of the tail distribution and Hill's estimator for shot noise sequence ⋮ Estimating Long Memory in Panel Random‐Coefficient AR(1) Data ⋮ Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data ⋮ Parameter estimation for first-order bifurcating autoregressive processes with Weibull innova\-tions ⋮ Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization ⋮ Change point test of tail index for autoregressive processes ⋮ Asymptotic normality of the likelihood moment estimators for a stationary linear process with heavy-tailed innovations ⋮ Estimation of a tail index based on minimum density power divergence ⋮ Adapting extreme value statistics to financial time series: dealing with bias and serial dependence ⋮ Some aspects of extreme value statistics under serial dependence ⋮ Weak convergence of the tail empirical process for dependent sequences ⋮ On tail index estimation based on multivariate data ⋮ Second-order regular variation, convolution and the central limit theorem ⋮ Extremal dependence measure for functional data ⋮ Least tail-trimmed squares for infinite variance autoregressions ⋮ Estimation of the index parameter for autoregressive data using the estimated innovations ⋮ Inference for the tail index of a GARCH(1,1) model and an AR(1) model with ARCH(1) errors ⋮ Moving-maximum models for extrema of time series ⋮ On the tail index inference for heavy-tailed GARCH-type innovations

This page was built for publication: Asymptotic behavior of hill's estimator for autoregressive data