Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/270029 
Year of Publication: 
2021
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 9 [Issue:] 1 [Article No.:] 1862445 [Year:] 2021 [Pages:] 1-18
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
This study compares the size and power of autoregressive conditional heteroskedasticity (ARCH) tests that are robust to the presence of a misspecified conditional mean. The approaches employed are based on two nonparametric regressions for the conditional mean: an ARCH test with a Nadaraya-Watson kernel regression and an ARCH test using a polynomial approximation regression. The two approaches do not require the specification of a conditional mean and can adapt to various nonlinear models, which are unknown a priori. The results reveal that the ARCH tests are robust to the misspecfied conditional mean models. The simulation results show that the ARCH tests based on the polynomial approximation regression approach have better properties of the size and power than those using the Nadaraya-Watson kernel regression approach for various nonlinear models.
Subjects: 
ARCH test
misspecified models
nonparametric regression
size and power
JEL: 
C12
C14
C58
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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