In this paper, we discuss the parameter estimation for a k-factor generalized long memory process with conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of...

In this paper, we discuss the parameter estimation for a k-factor generalized long memory processwith conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of...

In this paper, we discuss the parameter estimation for a k-factor generalized long memory process with conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of...

The possibility of confusing long memory behavior with structural changes need to specify what kind of long memory behavior is concerned in literature and applications. One attraction of long memory models is that they imply different long run predictions and effects of shocks to conventional...

In this paper we discuss different aspects of long memory behavior and applicable parametric models. We discuss the confusion that can arise when the empirical autocorrelation function decreases in a hyperbolic way.

In this paper, we propose an alternative approach to estimate long-term risk. Instead of using the static square root method, we use a dynamic approach based on volatility forecasting by non-linear models. We explore the possibility of improving the estimations by different models and...

In this paper, we propose an alternative approach to estimate long-term risk. Instead of using the static square root method, we use a dynamic approach based on volatility forecasting by non-linear models. We explore the possibility of improving the estimations by different models and...