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Evaluating power-law properties from data using a wavelet transform correlation method with applications to foreign exchange rates

Wei, H.L. and Billings, S.A. (2008) Evaluating power-law properties from data using a wavelet transform correlation method with applications to foreign exchange rates. Research Report. ACSE Research Report no. 979 . Automatic Control and Systems Engineering, University of Sheffield

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Abstract

Numerous studies in the literature have shown that the dynamics of many time series including observations in foreign exchange markets exhibit scaling behaviours. A novel statistical method, derived from the concept of the continuous wavelet transform correlation function (WTCF), is proposed for the evaluation of power-law properties from observed data. The new method reveals that foreign exchange rates obey power-laws and thus belong to the class of self-similarity processes.

Item Type: Monograph (Research Report)
Copyright, Publisher and Additional Information: The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords: Continuous wavelet transform, correlation function, foreign exchange rates, scaling law, power law, self-similarity.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports
Depositing User: Miss Anthea Tucker
Date Deposited: 15 Oct 2012 14:51
Last Modified: 05 Jun 2014 17:36
Status: Published
Publisher: Automatic Control and Systems Engineering, University of Sheffield
URI: http://eprints.whiterose.ac.uk/id/eprint/74636

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