AbstractWe investigate the extremal behavior of a special class of autoregressive processes with ARCH(1) errors given by the stochastic difference equationXn=αXn−1+β+λXn−12εn,n∈N,where (εn)n∈N are i.i.d. random variables. The extremes of such processes occur typically in clusters. We give an explicit formula for the extremal index and the probabilities for the length of a cluster
Extreme values for dependent data corresponding to high threshold ex-ceedences may occur in clusters...
The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. t...
The modelling of extremes of a time series has progressed from the assumption of independent observa...
AbstractWe investigate the extremal behavior of a special class of autoregressive processes with ARC...
We consider limit distributions of extremes of a process {Yn} satisfying the stochastic difference e...
AbstractWe consider limit distributions of extremes of a process {Yn} satisfying the stochastic diff...
We consider limit distributions of extremes of a process {Y,,} satisfying the stochastic difference ...
AbstractWe consider general nonstationary max-autoregressive sequences Xi, i ⩾ 1, with Xi = Zimax(Xi...
The study of clusters of extreme values of a time series (exceedances over a suf-ficiently high thre...
Generalised autoregressive conditional heteroskedastic (GARCH) processes have wide application in fi...
We consider extremal properties of Markov chains. Rootzén (1988) gives conditions for stationary, re...
Generalized Autoregressive Conditionally Heteroskedastic (GARCH) processes have become the standard ...
International audienceFor a wide class of stationary time series, extreme value theory provides limi...
The extremal index (θ) is the key parameter for extending extreme value theory results from IID to s...
In this paper we study the tail and the extremal behavior of stationary solutions of autoregressive ...
Extreme values for dependent data corresponding to high threshold ex-ceedences may occur in clusters...
The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. t...
The modelling of extremes of a time series has progressed from the assumption of independent observa...
AbstractWe investigate the extremal behavior of a special class of autoregressive processes with ARC...
We consider limit distributions of extremes of a process {Yn} satisfying the stochastic difference e...
AbstractWe consider limit distributions of extremes of a process {Yn} satisfying the stochastic diff...
We consider limit distributions of extremes of a process {Y,,} satisfying the stochastic difference ...
AbstractWe consider general nonstationary max-autoregressive sequences Xi, i ⩾ 1, with Xi = Zimax(Xi...
The study of clusters of extreme values of a time series (exceedances over a suf-ficiently high thre...
Generalised autoregressive conditional heteroskedastic (GARCH) processes have wide application in fi...
We consider extremal properties of Markov chains. Rootzén (1988) gives conditions for stationary, re...
Generalized Autoregressive Conditionally Heteroskedastic (GARCH) processes have become the standard ...
International audienceFor a wide class of stationary time series, extreme value theory provides limi...
The extremal index (θ) is the key parameter for extending extreme value theory results from IID to s...
In this paper we study the tail and the extremal behavior of stationary solutions of autoregressive ...
Extreme values for dependent data corresponding to high threshold ex-ceedences may occur in clusters...
The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. t...
The modelling of extremes of a time series has progressed from the assumption of independent observa...
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