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Time-varying dependence measures: a comparative analysis through wavelet approach

Time-varying dependence measures: a comparative analysis through wavelet approach PurposeThis paper aims to investigate the pattern of dependence between crude oil price and energy consumption of the most important economic sectors in the USA, over different time periods, using monthly data set from January 1986 to July 2014 and a comparative study between linear correlation versus copula correlation as a measure of dependence over the single scale and the multiscale analysis.Design/methodology/approachThe proposed method is based on the multiresolution analysis which gives more extensive and detailed description of the dependence price-consumption pattern over different periods of time.FindingsThe empirical results show that the dependence between variables is strongly sensitive to the time varying and generally increasing with time scale. In particular, the Pearson coefficients are less than the dependence copula measures. The single-scale analysis covers many time-varying dependences which are made clear, flexible and comprehensive by the description given by the multiscale approach. It explains better the structure of relationships between variables and helps understand the variations and improve forecasts of the crude oil price and energy consumption over different time scales.Originality/valueThe proposed methodology offers the opportunity to construct dynamic management strategies by taking into account the multiscale nature of crude oil price and consumption relationship. Moreover, the paper uses wavelets as a relatively new and powerful tool for statistical analysis in addition to the copula technique that allows a new understanding of variable correlation. The paper will be of interest not only for academics in the field of data dependencies analysis but also for fund managers and market investors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Energy Sector Management Emerald Publishing

Time-varying dependence measures: a comparative analysis through wavelet approach

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1750-6220
DOI
10.1108/IJESM-01-2016-0001
Publisher site
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Abstract

PurposeThis paper aims to investigate the pattern of dependence between crude oil price and energy consumption of the most important economic sectors in the USA, over different time periods, using monthly data set from January 1986 to July 2014 and a comparative study between linear correlation versus copula correlation as a measure of dependence over the single scale and the multiscale analysis.Design/methodology/approachThe proposed method is based on the multiresolution analysis which gives more extensive and detailed description of the dependence price-consumption pattern over different periods of time.FindingsThe empirical results show that the dependence between variables is strongly sensitive to the time varying and generally increasing with time scale. In particular, the Pearson coefficients are less than the dependence copula measures. The single-scale analysis covers many time-varying dependences which are made clear, flexible and comprehensive by the description given by the multiscale approach. It explains better the structure of relationships between variables and helps understand the variations and improve forecasts of the crude oil price and energy consumption over different time scales.Originality/valueThe proposed methodology offers the opportunity to construct dynamic management strategies by taking into account the multiscale nature of crude oil price and consumption relationship. Moreover, the paper uses wavelets as a relatively new and powerful tool for statistical analysis in addition to the copula technique that allows a new understanding of variable correlation. The paper will be of interest not only for academics in the field of data dependencies analysis but also for fund managers and market investors.

Journal

International Journal of Energy Sector Management – Emerald Publishing

Published: Jun 5, 2017

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