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

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  • Lukas Vacha
  • Jozef Barunik

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 cor- relations. 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 1/2 years 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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File URL: http://arxiv.org/pdf/1201.4776
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Bibliographic Info

Paper provided by arXiv.org in its series Papers with number 1201.4776.

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Date of creation: Jan 2012
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Publication status: Published in Energy Economics 34(1), pp. 241--247 (2012)
Handle: RePEc:arx:papers:1201.4776

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  1. Lanza, Alessandro & Manera, Matteo & McAleer, Michael, 2006. "Modeling dynamic conditional correlations in WTI oil forward and futures returns," Finance Research Letters, Elsevier, vol. 3(2), pages 114-132, June.
  2. 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.
  3. 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.
  4. Connor Jeff & Rossiter Rosemary, 2005. "Wavelet Transforms and Commodity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-22, March.
  5. Naccache, Théo, 2011. "Oil price cycles and wavelets," Energy Economics, Elsevier, vol. 33(2), pages 338-352, March.
  6. 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.
  7. 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.
  8. Ghoshray, Atanu & Johnson, Ben, 2010. "Trends in world energy prices," Energy Economics, Elsevier, vol. 32(5), pages 1147-1156, September.
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Citations

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Cited by:
  1. Aloui, Chaker & Hkiri, Besma, 2014. "Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis," Economic Modelling, Elsevier, vol. 36(C), pages 421-431.
  2. Gazi Salah Uddin & Mohamed Arouri & Aviral Kumar Tiwari, 2014. "Co-movements between Germany and International Stock Markets: Some New Evidence from DCC-GARCH and Wavelet Approaches," Working Papers 2014-143, Department of Research, Ipag Business School.
  3. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, Open Access Journal, vol. 5(4), pages 1018-1043, April.
  4. Jozef Barunik & Lukas Vacha, 2013. "Contagion among Central and Eastern European stock markets during the financial crisis," Papers 1309.0491, arXiv.org, revised Sep 2013.
  5. Jozef Barunik & Evzen Kocenda & Lukas Vacha, 2013. "Gold, Oil, and Stocks," Papers 1308.0210, arXiv.org, revised Mar 2014.
  6. Ladislav Kristoufek, 2013. "Fractal Markets Hypothesis and the Global Financial Crisis: Wavelet Power Evidence," Papers 1310.1446, arXiv.org.
  7. Lukas Vacha & Karel Janda & Ladislav Kristoufek & David Zilbermand, 2013. "Time-Frequency Dynamics of Biofuels-Fuels-Food System," CAMA Working Papers 2013-27, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  8. Reboredo, Juan C. & Rivera-Castro, Miguel A., 2014. "Wavelet-based evidence of the impact of oil prices on stock returns," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 145-176.
  9. Kristoufek, Ladislav & Janda, Karel & Zilberman, David, 2012. "Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective," Energy Economics, Elsevier, vol. 34(5), pages 1380-1391.
  10. Kristoufek, Ladislav & Janda, Karel & Zilberman, David, 2012. "Relationship Between Prices of Food, Fuel and Biofuel," 131st Seminar, September 18-19, 2012, Prague, Czech Republic 135793, European Association of Agricultural Economists.
  11. Luís Francisco Aguiar & Teresa Maria Rodrigues & Maria Joana Soares, 2012. "Oil Shocks and the Euro as an Optimum Currency Area," NIPE Working Papers 07/2012, NIPE - Universidade do Minho.
  12. Reboredo, Juan C. & Rivera-Castro, Miguel A., 2013. "A wavelet decomposition approach to crude oil price and exchange rate dependence," Economic Modelling, Elsevier, vol. 32(C), pages 42-57.
  13. Jozef Barunik & Michaela Barunikova, 2012. "Revisiting the fractional cointegrating dynamics of implied-realized volatility relation with wavelet band spectrum regression," Papers 1208.4831, arXiv.org, revised Feb 2013.
  14. Rita Sousa & Luís Aguiar-Conraria & Maria Joana Soares, 2014. "Carbon Financial Markets: a time-frequency analysis of CO2 price drivers," NIPE Working Papers 03/2014, NIPE - Universidade do Minho.

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