TAIL AND NONTAIL MEMORY WITH APPLICATIONS TO EXTREME VALUE AND ROBUST STATISTICS

DOI10.1017/S0266466610000514zbMath1401.62156MaRDI QIDQ5199499

Jonathan B. Hill

Publication date: 16 August 2011

Published in: Econometric Theory (Search for Journal in Brave)

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric robustness (62G35) Applications of statistics to actuarial sciences and financial mathematics (62P05) Central limit and other weak theorems (60F05) Stationary stochastic processes (60G10) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)

Related Items (10)

Quantile correlation coefficient: a new tail dependence measure ⋮ Multivariate Hill Estimators ⋮ GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference ⋮ Extremal Dependence-Based Specification Testing of Time Series ⋮ On tail index estimation using a sample with missing observations ⋮ Are there common values in first-price auctions? A tail-index nonparametric test ⋮ On the measurement and treatment of extremes in time series ⋮ Moment condition tests for heavy tailed time series ⋮ Jump tails, extreme dependencies, and the distribution of stock returns ⋮ Least tail-trimmed squares for infinite variance autoregressions

Cites Work

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• The extremogram: a correlogram for extreme events
• Extremal memory of stochastic volatility with an application to tail shape inference
• Regular variation of order 1 nonlinear AR-ARCH models
• Weak convergence of the tail empirical process for dependent sequences
• On functional central limit theorems for dependent, heterogeneous arrays with applications to tail index and tail dependence estimation
• What portion of the sample makes a partial sum asymptotically stable or normal?
• On asymptotic normality of Hill's estimator for the exponent of regular variation
• On the exceedance point process for a stationary sequence
• ARMAX model specification testing, with an application to unemployment in the Netherlands
• Asymptotic normality of trimmed sums of \(\Phi\)-mixing random variables
• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• A limit theorem for the maximum of autoregressive processes with uniform marginal distributions
• Tail-behavior of location estimators
• On tail index estimation using dependent data
• Asymptotic behavior of self-normalized trimmed sums: Nonnormal limits
• Stationarity of GARCH processes and of some nonnegative time series
• A simple general approach to inference about the tail of a distribution
• A maximal inequality and dependent strong laws
• Extremes of moving averages of stable processes
• Extremal index estimation for a weakly dependent stationary sequence
• Limit theory for bilinear processes with heavy-tailed noise
• On extremal distributions and sharp \(L_p\)-bounds for sums of multilinear forms
• A new weak dependence condition and applications to moment inequalities
• Regular variation of GARCH processes.
• Stability and the Lyapounov exponent of threshold AR-ARCH models
• The sample autocorrelations of heavy-tailed processes with applications to ARCH
• Extremal behaviour of solutions to a stochastic difference equation with applications to ARCH processes
• Limit theory for the sample autocorrelations and extremes of a GARCH \((1,1)\) process.
• Weak dependence beyond mixing and asymptotics for nonparametric regression
• A characterization of multivariate regular variation.
• The tail of the stationary distribution of an autoregressive process with \(\text{ARCH}(1)\) errors
• On maximum likelihood estimation of the extreme value index.
• The asymptotic joint distribution of self-normalized censored sums and sums of squares
• On linear processes with dependent innovations
• The asymptotic distribution of the trimmed mean
• Non-mixing properties of long memory processes
• Non-parametric Estimation of Tail Dependence
• GENERAL TRIMMED ESTIMATION: ROBUST APPROACH TO NONLINEAR AND LIMITED DEPENDENT VARIABLE MODELS
• A New Class of Models for Bivariate Joint Tails
• Stability of nonlinear AR-GARCH models
• Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case
• Extreme Value Theory for GARCH Processes
• Extremes of Stochastic Volatility Models
• Fractional differencing
• Basic structure of the asymptotic theory in dynamic nonlineaerco nometric models, part i: consistency and approximation concepts
• Tail Behavior of Regression Estimators and their Breakdown Points
• Calculating the extremal index for a class of stationary sequences
• The extremal index for a Markov chain
• Stochastic Limit Theory
• The maximum term of uniformly mixing stationary processes
• Almost sure relative stability of the maximum of a stationary sequence
• Diagnostics for Dependence within Time Series Extremes
• Extremes and local dependence in stationary sequences
• MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
• ON TAIL INDEX ESTIMATION FOR DEPENDENT, HETEROGENEOUS DATA
• On the probabilistic properties of a double threshold ARMA conditional heteroskedastic model
• STATIONARY ARCH MODELS: DEPENDENCE STRUCTURE AND CENTRAL LIMIT THEOREM
• ESTIMATION FOR A NONSTATIONARY SEMI-STRONG GARCH(1,1) MODEL WITH HEAVY-TAILED ERRORS
• WEAK DEPENDENCE: MODELS AND APPLICATIONS TO ECONOMETRICS
• On central limit theory for random additive functions under weak dependence restrictions
• Inference in Arch and Garch Models with Heavy-Tailed Errors
• Extreme Values in Uniformly Mixing Stationary Stochastic Processes
• On extreme values in stationary sequences
• Some Limit Theorems for Stationary Processes
• A Limit Theoerm for Random Variables with Infinite Moments
• Large deviations of sums of independent random variables

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