✦ LIBER ✦

GO-GARCH: a multivariate generalized orthogonal GARCH model

✍ Scribed by Roy van der Weide

Publisher: John Wiley and Sons
Year: 2002
Tongue: English
Weight: 163 KB
Volume: 17
Category: Article
ISSN: 0883-7252
DOI: 10.1002/jae.688

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

Multivariate GARCH specifications are typically determined by means of practical considerations such as the ease of estimation, which often results in a serious loss of generality. A new type of multivariate GARCH model is proposed, in which potentially large covariance matrices can be parameterized with a fairly large degree of freedom while estimation of the parameters remains feasible. The model can be seen as a natural generalization of the O‐GARCH model, while it is nested in the more general BEKK model. In order to avoid convergence difficulties of estimation algorithms, we propose to exploit unconditional information first, so that the number of parameters that need to be estimated by means of conditional information is more than halved. Both artificial and empirical examples are included to illustrate the model. Copyright © 2002 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

Multivariate GARCH models: a survey

✍ Luc Bauwens; Sébastien Laurent; Jeroen V. K. Rombouts 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 246 KB

## Abstract This paper surveys the most important developments in multivariate ARCH‐type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research. Copyright © 2006 John Wiley & Sons, Ltd.

Inference for some multivariate ARCH and

Inference for some multivariate ARCH and GARCH models

✍ I. D. Vrontos; P. Dellaportas; D. N. Politis 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 204 KB

## Abstract Multivariate time‐varying volatility models have attracted a lot of attention in modern finance theory. We provide an empirical study of some multivariate ARCH and GARCH models that already exist in the literature and have attracted a lot of practical interest. Bayesian and classical te

Multivariate GARCH models: software choi

Multivariate GARCH models: software choice and estimation issues

✍ Chris Brooks; Simon P. Burke; Gita Persand 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 90 KB

The development of multivariate generalized autoregressive conditionally heteroscedastic (MGARCH) models from the original univariate specifications represented a major step forward in the modelling of time series. MGARCH models permit time-varying conditional covariances as well as variances, and t

On a dynamic mixture GARCH model

✍ Xixin Cheng; Philip L. H. Yu; W. K. Li 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 284 KB

## Abstract This paper proposes a new mixture GARCH model with a dynamic mixture proportion. The mixture Gaussian distribution of the error can vary from time to time. The Bayesian Information Criterion and the EM algorithm are used to estimate the number of parameters as well as the model paramete

Average conditional correlation and tree

Average conditional correlation and tree structures for multivariate GARCH models

✍ Francesco Audrino; Giovanni Barone-Adesi 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 419 KB

We propose a simple class of multivariate GARCH models, allowing for timevarying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations.

Dynamic hedging with futures: A copula-b

Dynamic hedging with futures: A copula-based GARCH model

✍ Chih-Chiang Hsu; Chih-Ping Tseng; Yaw-Huei Wang 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 287 KB

## Abstract In a number of earlier studies it has been demonstrated that the traditional regression‐based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies have emerged. In this study the authors propose a class of ne