TY - JOUR
T1 - A comparison of alternative techniques for selecting an optimum ARCH model
AU - Mitchell, Heather
AU - McKenzie, Michael D.
PY - 2008
Y1 - 2008
N2 - Researchers have little to guide them when choosing an optimal model for use in auto regressive conditional heteroscedasticity modelling applications. Although the standard class of asymptotic model selection criteria may apply, some researchers have suggested that loss functions need to be developed, which are specific to each particular application. In this article, the relative merits of these two different techniques are considered. The results suggest that the model selection criteria provide superior results, although none of the techniques work well when the data are characterized by power and leverage effects.
AB - Researchers have little to guide them when choosing an optimal model for use in auto regressive conditional heteroscedasticity modelling applications. Although the standard class of asymptotic model selection criteria may apply, some researchers have suggested that loss functions need to be developed, which are specific to each particular application. In this article, the relative merits of these two different techniques are considered. The results suggest that the model selection criteria provide superior results, although none of the techniques work well when the data are characterized by power and leverage effects.
KW - ARCH
KW - Model selection criteria
UR - http://www.scopus.com/inward/record.url?scp=38349131030&partnerID=8YFLogxK
U2 - 10.1080/10629360600932857
DO - 10.1080/10629360600932857
M3 - Article
AN - SCOPUS:38349131030
VL - 78
SP - 51
EP - 67
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
SN - 0094-9655
IS - 1
ER -