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Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis

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  • Vacha, Lukas
  • Barunik, Jozef

Abstract

In this paper, we contribute to the literature on energy market co-movement by studying its dynamics in the time-frequency domain. The novelty of our approach lies in the application of wavelet tools to commodity market data. A major part of economic time series analysis is done in the time or frequency domain separately. Wavelet analysis combines these two fundamental approaches allowing study of the time series in the time-frequency domain. Using this framework, we propose a new, model-free way of estimating time-varying correlations. In the empirical analysis, we connect our approach to the dynamic conditional correlation approach of Engle (2002) on the main components of the energy sector. Namely, we use crude oil, gasoline, heating oil, and natural gas on a nearest-future basis over a period of approximately 16 and 1/2years beginning on November 1, 1993 and ending on July 21, 2010. Using wavelet coherence, we uncover interesting dynamics of correlations between energy commodities in the time-frequency space.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 34 (2012)
Issue (Month): 1 ()
Pages: 241-247

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Handle: RePEc:eee:eneeco:v:34:y:2012:i:1:p:241-247

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Web page: http://www.elsevier.com/locate/eneco

Related research

Keywords: Correlation; Co-movement; Wavelet analysis; Wavelet coherence;

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  1. Connor Jeff & Rossiter Rosemary, 2005. "Wavelet Transforms and Commodity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-22, March.
  2. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  3. Ghoshray, Atanu & Johnson, Ben, 2010. "Trends in world energy prices," Energy Economics, Elsevier, vol. 32(5), pages 1147-1156, September.
  4. Martina, Esteban & Rodriguez, Eduardo & Escarela-Perez, Rafael & Alvarez-Ramirez, Jose, 2011. "Multiscale entropy analysis of crude oil price dynamics," Energy Economics, Elsevier, vol. 33(5), pages 936-947, September.
  5. António Rua & Luís Catela Nunes, 2009. "International comovement of stock market returns: a wavelet analysis," Working Papers w200904, Banco de Portugal, Economics and Research Department.
  6. Aguiar-Conraria, Luís & Azevedo, Nuno & Soares, Maria Joana, 2008. "Using wavelets to decompose the time–frequency effects of monetary policy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2863-2878.
  7. Matteo Manera & Alessandro Lanza & Michael McAleer, 2004. "Modelling Dynamic Conditional Correlations in WTI Oil Forward and Futures Returns," Working Papers 2004.72, Fondazione Eni Enrico Mattei.
  8. Naccache, Théo, 2011. "Oil price cycles and wavelets," Energy Economics, Elsevier, vol. 33(2), pages 338-352, March.
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