CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST 2010, 26(2) 207-219 DOI:      ISSN: 1001-4268 CN: 31-1256

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Keywords
Change points in volatility
wavelet coefficient
kernel estimation
local polynomial smoother.
Authors
Wang Jingle
Liu Weiqi
PubMed
Article by
Article by

Wavelet Identification of Structural Change Points in Volatility Models for Time Series

Wang Jingle,Liu Weiqi

School of Mathematical Sciences,Shanxi University

Abstract��

We propose two estimators, an integral
estimator and a discretized estimator, for the wavelet coefficient
of volatility in time series models. These estimators can be used to
detect the changes of volatility in time series models. The location
estimators of the jump points, we proposed, have been shown to have
the minimax convergence rate, which is the optimal rate for the
estimation of change points. The jump sizes and locations of change
points can be consistently estimated by wavelet coefficients. The
convergency rates of these estimators are derived and the asymptotic
distributions of the statistics are established.

Keywords�� Change points in volatility   wavelet coefficient   kernel estimation   local polynomial smoother.  
Received 1900-01-01 Revised 1900-01-01 Online:  
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Corresponding Authors: Wang Jingle
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1��Wang Jingle, Zheng Ming.Detection of Change Points in Volatility of Non-Parametric Regression by Wavelets[J]. CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST, 2012,28(4): 413-438

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