TY - JOUR
T1 - GARCH model selection criteria
AU - Mitchell, Heather
AU - McKenzie, Michael D.
PY - 2003/8
Y1 - 2003/8
N2 - The autoregressive conditional heteroscedasticity (ARCH) family of models has grown to encompass a wide range of specifications, each of which is designed to enhance the ability of the model to capture the characteristics of the data. In this paper, the ability of a number of model selection criteria to correctly identify the data generating process in simulated data is established. The results of this study suggest that the Hannan-Quinn and stochastic complexity criteria provide a superior level of performance for ARCH and generalized ARCH (GARCH) processes compared to the more commonly used criteria. Where leverage and/or power effects are present, however, none of the procedures considered perform well. A new LM based test for the presence of nonlinearity and power effects is introduced and tested.
AB - The autoregressive conditional heteroscedasticity (ARCH) family of models has grown to encompass a wide range of specifications, each of which is designed to enhance the ability of the model to capture the characteristics of the data. In this paper, the ability of a number of model selection criteria to correctly identify the data generating process in simulated data is established. The results of this study suggest that the Hannan-Quinn and stochastic complexity criteria provide a superior level of performance for ARCH and generalized ARCH (GARCH) processes compared to the more commonly used criteria. Where leverage and/or power effects are present, however, none of the procedures considered perform well. A new LM based test for the presence of nonlinearity and power effects is introduced and tested.
UR - http://www.scopus.com/inward/record.url?scp=0348156079&partnerID=8YFLogxK
U2 - 10.1088/1469-7688/3/4/303
DO - 10.1088/1469-7688/3/4/303
M3 - Article
AN - SCOPUS:0348156079
SN - 1469-7688
VL - 3
SP - 262
EP - 284
JO - Quantitative Finance
JF - Quantitative Finance
IS - 4
ER -