This paper provides a review of some recent theoretical results for time series models with
GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model …

Long-memory, or more generally fractal, processes are known to play an important role in
many scientific disciplines and applied fields such as physics, geophysics, hydrology …

Provides a comprehensive and updated study of GARCH models and their applications in
finance, covering new developments in the discipline This book provides a comprehensive …

In this article we propose a new multivariate generalized autoregressive conditional
heteroscedasticity (MGARCH) model with time-varying correlations. We adopt the vech …

This paper investigates the asymptotic theory for a vector autoregressive moving average–
generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model. The …

We prove the strong consistency and asymptotic normality of the quasi-maximum likelihood
estimator of the parameters of pure generalized autoregressive conditional heteroscedastic …

The modeling of stochastic dependence is fundamental for understanding random systems
evolving in time. When measured through linear correlation, many of these systems exhibit a …

A self-contained, contemporary treatment of the analysis of long-range dependent data Long-
Memory Time Series: Theory and Methods provides an overview of the theory and methods …

This paper first provides some useful results on a generalized random coefficient
autoregressive model and a generalized hidden Markov model. These results …

Econometric modelling of financial data received a broad interest in the last 20 years and
the literature on ARCH and related models is vast. Starting with the path breaking works by …