ANALYSIS OF EFFECT OF DETRENDING OF TIME-SCALE STRUCTURE OF FINANCIAL DATA USING DISCRETE WAVELET TRANSFORM

Brian J. W. Fleming (), Dejin Yu, Robert G. Harrison and David Jubb
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Brian J. W. Fleming: Edinburgh Multidisciplinary Consortium for Advanced Nonlinear Analysis of Complex Systems, Department of Physics, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK
Dejin Yu: Edinburgh Multidisciplinary Consortium for Advanced Nonlinear Analysis of Complex Systems, Department of Physics, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK
Robert G. Harrison: Edinburgh Multidisciplinary Consortium for Advanced Nonlinear Analysis of Complex Systems, Department of Physics, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK
David Jubb: Standard Life Investments, 1 George Street, Edinburgh EH2 2LL, UK

International Journal of Theoretical and Applied Finance (IJTAF), 2000, vol. 03, issue 03, 375-379

Abstract: One of the key features of wavelet analysis is its ability to decompose non-stationary signals according to time and scale. In this work, we use discrete wavelets to analyze the influence of detrending techniques on the time-scale information structure of daily financial data. We examine the use of log returns, a linear trend and the Hodrick–Prescott (HP) filter. Quantitative measurements of information distortion are given using the mean-squared error (MSE) and correlation of the wavelet coefficients between the detrended and original data. We find that log returns and linear detrending are most distortional. We also conclude that the HP-filter is most effective, depending on appropriate selection of the filter parameter, λ, which is${\mathcal O} (10^{11})$for the given data set.

Date: 2000
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DOI: 10.1142/S0219024900000231

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