Extremal memory of stochastic volatility with an application to tail shape inference
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Publication:607175
DOI10.17615/fxgd-ww17 10.1016/j.jspi.2010.07.007; 10.17615/fxgd-ww17zbMath1209.62245OpenAlexW4300701408MaRDI QIDQ607175
Publication date: 19 November 2010
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.17615/fxgd-ww17
Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistics of extreme values; tail inference (62G32)
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Cites Work
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- The extremogram: a correlogram for extreme events
- Tail estimates motivated by extreme value theory
- Bivariate extreme statistics. I
- Estimation of the extreme-value index and generalized quantile plots
- On functional central limit theorems for dependent, heterogeneous arrays with applications to tail index and tail dependence estimation
- Extremes and related properties of random sequences and processes
- Convolution tails, product tails and domains of attraction
- On asymptotic normality of Hill's estimator for the exponent of regular variation
- Estimating tails of probability distributions
- On tail index estimation using dependent data
- Stationarity of GARCH processes and of some nonnegative time series
- Statistical inference using extreme order statistics
- A simple general approach to inference about the tail of a distribution
- A maximal inequality and dependent strong laws
- Extremes of stochastic volatility models
- Mixing: Properties and examples
- Regular variation of GARCH processes.
- Generalized autoregressive conditional heteroscedasticity
- 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.
- A characterization of multivariate regular variation.
- On maximum likelihood estimation of the extreme value index.
- Second-order regular variation and rates of convergence in extreme-value theory
- Temporal aggregation of volatility models
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- Non-strong mixing autoregressive processes
- Non-parametric Estimation of Tail Dependence
- Stability of nonlinear AR-GARCH models
- Extreme Value Theory for GARCH Processes
- Extremes of Stochastic Volatility Models
- SLOW VARIATION WITH REMAINDER: THEORY AND APPLICATIONS
- Statistics for near independence in multivariate extreme values
- Uniform rates of convergence in extreme-value theory
- Calculating the extremal index for a class of stationary sequences
- On asymptotic normality of the hill estimator
- Tail Index Estimation, Pareto Quantile Plots, and Regression Diagnostics
- The maximum term of uniformly mixing stationary processes
- Diagnostics for Dependence within Time Series Extremes
- Extremes and local dependence in stationary sequences
- IX. On the problem of the most efficient tests of statistical hypotheses
- MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
- A Conditional Approach for Multivariate Extreme Values (with Discussion)
- Consistency of Hill's estimator for dependent data
- Statistical Methods for Multivariate Extremes: An Application to Structural Design
- ON TAIL INDEX ESTIMATION FOR DEPENDENT, HETEROGENEOUS DATA
- STATIONARY ARCH MODELS: DEPENDENCE STRUCTURE AND CENTRAL LIMIT THEOREM
- WEAK DEPENDENCE: MODELS AND APPLICATIONS TO ECONOMETRICS
- Asymptotic Independence of Bivariate Extremes
- Extreme Values in Uniformly Mixing Stationary Stochastic Processes
- On extreme values in stationary sequences
- Extreme Values in Samples from $m$-Dependent Stationary Stochastic Processes
