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

Econometrics

Yongning Wang¹ 

[1] id="af1-econometrics-01-00001">Booth School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, IL 60637, U

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

DOI  :  10.3390/econometrics1010001

来源: mdpi

【 摘 要 】

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, calledin 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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.