Nonparametric estimation of structural change points in volatility models for time series

    Authors

    G. M. Chen; Y. K. Choi;Y. Zhou

    Comments

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    Abbreviated Journal Title

    J. Econom.

    Keywords

    change points in volatility; least squares; nonparametric estimation; asymptotic properties; INDEPENDENT RANDOM-VARIABLES; CONDITIONAL HETEROSCEDASTICITY; VARIANCE; LIKELIHOOD; JUMPS; REGRESSION; INFERENCE; SEQUENCE; SUMS; Economics; Mathematics, Interdisciplinary Applications; Social Sciences, ; Mathematical Methods

    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. (C) 2004 Elsevier B.V. All rights reserved

    Journal Title

    Journal of Econometrics

    Volume

    126

    Issue/Number

    1

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    79

    Last Page

    114

    WOS Identifier

    WOS:000226532600004

    ISSN

    0304-4076

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