On the approximate decorrelation property of the discrete wavelet transform for fractionally differenced processes

In this correspondence, we develop an asymptotic theory for the correlations of wavelet coefficients of the discrete wavelet transform (DWT) for fractionally differenced processes. It provides a theoretical justification for the approximate decorrelation property of the DWT for fractionally differenced processes. In addition, it provides insights on how the length of the wavelet filter affects the within scale correlations and the between scale correlations differently; for within scale correlations, increasing the length of the wavelet filter increases the rate of decay as the two wavelet coefficients get further apart, while for between scale correlations, using a wavelet filter that is long enough can reduce the between scale correlations even for wavelet coefficients that are close together.

[1]  Elias Masry,et al.  The wavelet transform of stochastic processes with stationary increments and its application to fractional Brownian motion , 1993, IEEE Trans. Inf. Theory.

[2]  Peter Guttorp,et al.  Wavelet-based parameter estimation for polynomial contaminated fractionally differenced processes , 2005, IEEE Transactions on Signal Processing.

[3]  A. Oppenheim,et al.  Signal processing with fractals: a wavelet-based approach , 1996 .

[4]  Mark J. Jensen An Approximate Wavelet MLE of Short- and Long-Memory Parameters , 1999 .

[5]  Ming-Jun Lai,et al.  On the digital filter associated with Daubechies' wavelets , 1995, IEEE Trans. Signal Process..

[6]  Mark J. Jensen An Alternative Maximum Likelihood Estimator of Long-Memeory Processes Using Compactly Supported Wavelets , 1997 .

[7]  Brandon J. Whitcher,et al.  Assessing Nonstationary Time Series Using Wavelets , 1998 .

[8]  Mark J. Jensen Using wavelets to obtain a consistent ordinary least squares estimator of the long-memory parameter , 1997 .

[9]  Patrick Flandrin,et al.  Wavelet analysis and synthesis of fractional Brownian motion , 1992, IEEE Trans. Inf. Theory.

[10]  A.H. Tewfik,et al.  Correlation structure of the discrete wavelet coefficients of fractional Brownian motion , 1992, IEEE Trans. Inf. Theory.

[12]  Mark J. Jensen Bayesian Inference of Long-Memory Dependence in Volatility via Wavelets , 2000 .

[13]  P. Phillips Econometric Analysis of Fisher's Equation , 2005 .

[14]  T. Kawata Fourier analysis in probability theory , 1972 .

[15]  A. Walden,et al.  Wavelet Analysis and Synthesis of Stationary Long-Memory Processes , 1996 .

[16]  A. Walden,et al.  Wavelet Methods for Time Series Analysis , 2000 .

[17]  Peter Guttorp,et al.  The Impact of Wavelet Coefficient Correlations on Fractionally Differenced Process Estimation , 2001 .

[18]  Richard T. Baillie,et al.  Long memory processes and fractional integration in econometrics , 1996 .

[19]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[20]  G. Wornell Wavelet-based representations for the 1/f family of fractal processes , 1993, Proc. IEEE.