The authors propose a simplified multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model (the S-GARCH model), which involves the estimation of only univariate GARCH models, both for the individual return series and for the sum and difference of each pair of series. The covariance between each pair of return series is then imputed from these variance estimates. The proposed model is considerably easier to estimate than existing multivariate GARCH models and does not suffer from the convergence problems that characterize many of these models. Moreover, the model can be easily extended to include more complex dynamics or alternative forms of the GARCH specification. The S-GARCH model is used to estimate the minimum...
The main aim of this article is to investigate the accuracy of the Multivariate Generalized Autoregr...
In the past thirty years, academia and the marketplace have devoted significant effort and resources...
Abstract: This study compares the fit and forecast performance of a selected group of parametric Gen...
The authors propose a simplified multivariate GARCH (generalized autoregressive conditional heterosc...
Author's draft dated October 2004 issued as XFi working paperThe authors propose a simplified multiv...
The main aim of this article is to investigate the accuracy of the Multivariate Generalized Autoregr...
This paper compares a standard GARCH model with a Constant Elasticity of Variance GARCH model across...
This paper addresses the question of the selection of multivariate generalized autoregressive condit...
This thesis contributes four essays to the economic literature on the multivariate modeling of the v...
ii Autoregressive and Moving Average time series models and their combination are reviewed. Autoregr...
This master thesis deals with extension of the univariate GARCH model to multivari- ate models. We p...
The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a c...
We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model ...
The objective of this paper is to implement and test the multivariate regime-switching GARCH model a...
Abstract: The univariate Generalised Autoregressive Conditional Heterscedasticity (GARCH) model has ...
The main aim of this article is to investigate the accuracy of the Multivariate Generalized Autoregr...
In the past thirty years, academia and the marketplace have devoted significant effort and resources...
Abstract: This study compares the fit and forecast performance of a selected group of parametric Gen...
The authors propose a simplified multivariate GARCH (generalized autoregressive conditional heterosc...
Author's draft dated October 2004 issued as XFi working paperThe authors propose a simplified multiv...
The main aim of this article is to investigate the accuracy of the Multivariate Generalized Autoregr...
This paper compares a standard GARCH model with a Constant Elasticity of Variance GARCH model across...
This paper addresses the question of the selection of multivariate generalized autoregressive condit...
This thesis contributes four essays to the economic literature on the multivariate modeling of the v...
ii Autoregressive and Moving Average time series models and their combination are reviewed. Autoregr...
This master thesis deals with extension of the univariate GARCH model to multivari- ate models. We p...
The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a c...
We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model ...
The objective of this paper is to implement and test the multivariate regime-switching GARCH model a...
Abstract: The univariate Generalised Autoregressive Conditional Heterscedasticity (GARCH) model has ...
The main aim of this article is to investigate the accuracy of the Multivariate Generalized Autoregr...
In the past thirty years, academia and the marketplace have devoted significant effort and resources...
Abstract: This study compares the fit and forecast performance of a selected group of parametric Gen...