Academic Journals Database
Disseminating quality controlled scientific knowledge

On Diagnostic Checking of Vector ARMA-GARCH Models with Gaussian and Student-t Innovations

ADD TO MY LIST
 
Author(s): Yongning Wang | Ruey S. Tsay

Journal: Econometrics
ISSN 2225-1146

Volume: 1;
Issue: 1;
Start page: 1;
Date: 2013;
Original page

Keywords: Vector autoregressive moving-average process | multivariate GARCH model | asymptotic distribution | portmanteau statistic | model checking | heavy tail | multivariate time series | bootstrap

ABSTRACT
This paper focuses on the diagnostic checking of vector ARMA (VARMA) models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the cross-product vector of standardized residuals. This is different from the traditional approach that employs only the squared series of standardized residuals. We then study two portmanteau statistics, called Q1(M) and Q2(M), for model checking. A residual-based bootstrap method is provided and demonstrated as an effective way to approximate the diagnostic checking statistics. Simulations are used to compare the performance of the proposed statistics with other methods available in the literature. In addition, we also investigate the effect of GARCH shocks on checking a fitted VARMA model. Empirical sizes and powers of the proposed statistics are investigated and the results suggest a procedure of using jointly Q1(M) and Q2(M) in diagnostic checking. The bivariate time series of FTSE 100 and DAX index returns is used to illustrate the performance of the proposed portmanteau statistics. The results show that it is important to consider the cross-product series of standardized residuals and GARCH effects in model checking.

Tango Jona
Tangokurs Rapperswil-Jona

    
RPA Switzerland

RPA Switzerland

Robotic process automation