A simple multivariate ARCH model specified by random coefficients

Authors:
P. W. Fong;W. K. Li;Hong-Zhi An
Affiliations:
Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong;Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong;Institute of Applied Mathematics, Academia Sinica, Beijing, China
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 0
Cited 3

Preface: Special Issue on Nonlinear Modelling and Financial Econometrics

Computational Statistics & Data Analysis
Deriving the autocovariances of powers of Markov-switching GARCH models, with applications to statistical inference

Computational Statistics & Data Analysis
Identifying financial time series with similar dynamic conditional correlation

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

This paper provides an alternative formulation of the conditional correlation structure in fitting the multivariate GARCH model. A special case is the multivariate ARCH model with random coefficients. Its coherence structure is derived by the correlations between the random coefficients which play an important role in describing the interested heteroscedastic features. The parameter estimation problem can be solved by maximum likelihood estimation and model selection is via the likelihood ratio test. We consider three real applications: (1) the spot and forward rates of the Deutsche Mark against the US dollars; (2) exchange rates of Deutsche Mark and Japanese Yen against US dollars; (3) the Heng Sang index and SES index.