Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28713
Title: Nonparametric estimation of structural change points in volatility models for time series
Authors: Chen, G
Choi, YK
Zhou, Y
Keywords: Asymptotic properties
Change points in volatility
Least squares
Nonparametric estimation
Issue Date: 2005
Source: Journal of econometrics, 2005, v. 126, no. 1, p. 79-114 How to cite?
Journal: Journal of Econometrics 
Abstract: We propose a hybrid estimation procedure that combines the least squares and nonparametric methods to estimate change points of volatility in time series models. Its main advantage is that it does not require any specific form of marginal or transitional densities of the process. We also establish the asymptotic properties of the estimators when the regression and conditional volatility functions are not known. The proposed tests for change points of volatility are shown to be consistent and more powerful than the nonparametric ones in the literature. Finally, we provide simulations and empirical results using the Hong Kong stock market index (HSI) series.
URI: http://hdl.handle.net/10397/28713
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2004.02.008
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

32
Last Week
0
Last month
0
Citations as of Jan 16, 2017

WEB OF SCIENCETM
Citations

20
Last Week
0
Last month
0
Citations as of Jan 12, 2017

Page view(s)

22
Last Week
0
Last month
Checked on Jan 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.