Wavelet analysis of nonlinear long-range dependent processes. Applications to financial time series

13Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.

You may have access to this PDF.

Abstract

We present and study the performance of the semiparametric wavelet estimator for the long-memory parameter devised by [Veitch and Abry (1999)]. We compare this estimator with two semiparametric estimators in the spectral domain, the local Whittle (LW) estimator developed by [Robinson (1995a)] and the log-periodogram (LP) estimator by [Geweke and Porter-Hudak (1983)]. The wavelet estimator performs well for a wide range of nonlinear long-memory processes in the conditional mean and the conditional variance, and is reliable for discriminating between change-points and long-range dependence in volatility. We also address the issue of selection of the range of octaves used as regressors by the weighted least squares estimator. We will see that using the feasible optimal bandwidths for either the LW and LP estimators, respectively studied by [Henry and Robinson (1996) and [Henry (2001)], is a useful rule of thumb for selecting the lowest octave. We apply the wavelet estimator to volatility series of high frequency (intra-day) Foreign Exchange (FX) rates, and to the volatility and volume of stocks of the Dow Jones Industrial Average Index. © 2007 Springer Berlin Heidelberg.

Cite

CITATION STYLE

APA

Teyssière, G., & Abry, P. (2007). Wavelet analysis of nonlinear long-range dependent processes. Applications to financial time series. In Long Memory in Economics (pp. 173–238). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-34625-8_7

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free