Title

Detections Of Changes In Return By A Wavelet Smoother With Conditional Heteroscedastic Volatility

Keywords

α-Mixing; Change points; Conditional heteroscedastic variance; Kernel estimation; Local polynomial smoother; Nonparametric regression; Wavelet coefficient

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. © 2007 Elsevier B.V. All rights reserved.

Publication Date

4-1-2008

Publication Title

Journal of Econometrics

Volume

143

Issue

2

Number of Pages

227-262

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jeconom.2007.10.001

Socpus ID

38949212236 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/38949212236

This document is currently not available here.

Share

COinS