Detections of changes in return by a wavelet smoother with conditional heteroscedastic volatility

    Authors

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

    Comments

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    Abbreviated Journal Title

    J. Econom.

    Keywords

    nonparametric regression; wavelet coefficient; change points; kernel; estimation; local polynomial smoother; conditional heteroscedastic; variance; alpha-mixing; CHANGE-POINTS; NONPARAMETRIC REGRESSION; TERM STRUCTURE; TIME-SERIES; MODELS; JUMP; IDENTIFICATION; DIFFUSION; VARIANCE; Economics; Mathematics, Interdisciplinary Applications; Social Sciences, ; Mathematical Methods

    Abstract

    In this paper, we propose two estimators, an integral estimator and a discretized estimator, for the wavelet coefficient of regression functions in nonparametric regression models with heteroscedastic variance. These estimators can be used to test the jumps of the regression function. The model allows for lagged-dependent variables and other mixing regressors. The asymptotic distributions of the statistics are established, and the asymptotic critical values are analytically obtained from the asymptotic distribution. We also use the test to determine consistent estimators for the locations of change points. The jump sizes and locations of change points can be consistently estimated using wavelet coefficients, and the convergency rates of these estimators are derived. We perform some Monte Carlo simulations to check the powers and sizes of the test statistics. Finally, we give practical examples in finance and economics to detect changes in stock returns and short-term interest rates using the empirical wavelet method. (C) 2007 Elsevier B.V. All rights reserved.

    Journal Title

    Journal of Econometrics

    Volume

    143

    Issue/Number

    2

    Publication Date

    1-1-2008

    Document Type

    Article

    Language

    English

    First Page

    227

    Last Page

    262

    WOS Identifier

    WOS:000254090400001

    ISSN

    0304-4076

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