We examine the auto-dependence structure of strictly stationary solutions of linear stochastic recurrence equations and of strictly stationary GARCH(1, 1) processes from the point of view of ordinary and generalized tail dependence coefficients. Since such processes can easily be of infinite variance, a substitute for the usual auto-correlation function is needed
In the study of random processes, dependence is the rule rather than the exception. To facilitate th...
We collect some of the probabilistic properties of a strictly stationary stochas-tic volatility proc...
We show that the finite-dimensional distributions of a GARCH process are regularly varying, i.e., th...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
We study the sample autocovariance and autocorrelation function of the stationary AR(1) process with...
Serial dependence in non-linear time series cannot always be reliably quantified using linear autoco...
Extreme values of a stationary, multivariate time series may exhibit dependence across coordinates a...
AbstractWe show that the finite-dimensional distributions of a GARCH process are regularly varying, ...
AbstractExtreme values of a stationary, multivariate time series may exhibit dependence across coord...
The asymptotic theory for the sample autocorrelations and extremes of a GARCH(I, 1) process is provi...
We are interested in the theoretical properties of Stochastic Recurrent Equations (SRE) and their ap...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
We study bivariate stochastic recurrence equations with triangular matrix coefficients and we charac...
Multivariate process satisfying affine stochastic recurrence equation with generic diagonal matrices...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
In the study of random processes, dependence is the rule rather than the exception. To facilitate th...
We collect some of the probabilistic properties of a strictly stationary stochas-tic volatility proc...
We show that the finite-dimensional distributions of a GARCH process are regularly varying, i.e., th...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
We study the sample autocovariance and autocorrelation function of the stationary AR(1) process with...
Serial dependence in non-linear time series cannot always be reliably quantified using linear autoco...
Extreme values of a stationary, multivariate time series may exhibit dependence across coordinates a...
AbstractWe show that the finite-dimensional distributions of a GARCH process are regularly varying, ...
AbstractExtreme values of a stationary, multivariate time series may exhibit dependence across coord...
The asymptotic theory for the sample autocorrelations and extremes of a GARCH(I, 1) process is provi...
We are interested in the theoretical properties of Stochastic Recurrent Equations (SRE) and their ap...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
We study bivariate stochastic recurrence equations with triangular matrix coefficients and we charac...
Multivariate process satisfying affine stochastic recurrence equation with generic diagonal matrices...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
In the study of random processes, dependence is the rule rather than the exception. To facilitate th...
We collect some of the probabilistic properties of a strictly stationary stochas-tic volatility proc...
We show that the finite-dimensional distributions of a GARCH process are regularly varying, i.e., th...