Structural Change and long memory in the GARCH(1,1)-model

It has long been known that the estimated persistence parameter in the GARCH(1,1) - model is biased upwards when the parameters of the model are not constant throughout the sample. The present paper explains the mechanics of this behavior for a particular class of estimates of the model parameters. It gives sufficient conditions for the estimated persistence to tend to one when the mean of the process changes, both for a given sample size (as the size of the structural change increases), and as sample size increases, extending previous results that were concerned with changes in the volatility parameters. --structural change,long memory,GARCH

Structural Change and long memory in the GARCH(1,1)-model

It has long been known that the estimated persistence parameter in the GARCH(1,1) - model is biased upwards when the parameters of the model are not constant throughout the sample. The present paper explains the mechanics of this behavior for a particular class of estimates of the model parameters. It gives sufficient conditions for the estimated persistence to tend to one when the mean of the process changes, both for a given sample size (as the size of the structural change increases), and as sample size increases, extending previous results that were concerned with changes in the volatility parameters

Structural change and long memory in the GARCH(1,1)-model

It has long been known that the estimated persistence parameter in the GARCH(1,1) - model is biased upwards when the parameters of the model are not constant throughout the sample. The present paper explains the mechanics of this behavior for a particular class of estimates of the model parameters. It gives sufficient conditions for the estimated persistence to tend to one when the mean of the process changes, both for a given sample size (as the size of the structural change increases), and as sample size increases, extending previous results that were concerned with changes in the volatility parameters

On the origins of high persistence in GARCH-models

The estimated persistence in various types of GARCH-models is known to be too large when the parameters of the model undergo structural changes somewhere in the sample. The present paper adds further insights into this phenomenon for the Baillie and Chung (2001) minimum distance estimates of the model parameters. While previous research has focused on the effects of changes in the GARCH-parameters, we investigate here the consequences of a changing mean