The limit theory of a change-point process which is based on the Manhattan distance of the sample autocorrelation function with applications to GARCH processes is examined. The general theory of the sample autocovariance and sample autocorrelation functions of a stationary GARCH process forms the basis of this study. Specifically the point processes theory is utilized to obtain their weak convergence limit at different lags. This is further extended to the change-point process. The limits are found to be generally random as a result of the infinite variance
Abstract§ GARCH processes constitute the major area of time series variance analysis hence the limit...
AbstractTheorems of approximation of Gaussian processes for the sequential empirical process of the ...
AbstractWe show that the finite-dimensional distributions of a GARCH process are regularly varying, ...
GARCH models have been commonly used to capture volatility dynamics in financial time series. A key ...
The asymptotic theory for the sample autocorrelations and extremes of a GARCH(I, 1) process is provi...
The instability of volatility parameters in GARCH models is an important issue for analyzing financi...
Abstract This paper first develops a general theory for estimating change-points in a general class ...
Generalized autoregressive conditionally heteroskedastic (GARCH) processes are widely used for model...
Generalized Auto-regressive Conditional Heteroskedastic (GARCH) models with fixed parameters are typ...
[[abstract]]This paper shows how the parameters of a stable GARCH(1, 1) model can be estimated from ...
Change-point models are widely used by statisticians to model drastic changes in the pattern of obse...
Many econometric time series data sets, such as log returns of stocks, exhibit evidence of the so ca...
We show that the finite-dimensional distributions of a GARCH process are regularly varying, i.e., th...
summary:Recently Hu\v{s}ková (1998) has studied the least squares estimator of a change-point in gra...
GARCH models are called ‘strong’ or ‘weak’ depending on the presence of parametric distributional as...
Abstract§ GARCH processes constitute the major area of time series variance analysis hence the limit...
AbstractTheorems of approximation of Gaussian processes for the sequential empirical process of the ...
AbstractWe show that the finite-dimensional distributions of a GARCH process are regularly varying, ...
GARCH models have been commonly used to capture volatility dynamics in financial time series. A key ...
The asymptotic theory for the sample autocorrelations and extremes of a GARCH(I, 1) process is provi...
The instability of volatility parameters in GARCH models is an important issue for analyzing financi...
Abstract This paper first develops a general theory for estimating change-points in a general class ...
Generalized autoregressive conditionally heteroskedastic (GARCH) processes are widely used for model...
Generalized Auto-regressive Conditional Heteroskedastic (GARCH) models with fixed parameters are typ...
[[abstract]]This paper shows how the parameters of a stable GARCH(1, 1) model can be estimated from ...
Change-point models are widely used by statisticians to model drastic changes in the pattern of obse...
Many econometric time series data sets, such as log returns of stocks, exhibit evidence of the so ca...
We show that the finite-dimensional distributions of a GARCH process are regularly varying, i.e., th...
summary:Recently Hu\v{s}ková (1998) has studied the least squares estimator of a change-point in gra...
GARCH models are called ‘strong’ or ‘weak’ depending on the presence of parametric distributional as...
Abstract§ GARCH processes constitute the major area of time series variance analysis hence the limit...
AbstractTheorems of approximation of Gaussian processes for the sequential empirical process of the ...
AbstractWe show that the finite-dimensional distributions of a GARCH process are regularly varying, ...