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Diversification in Crude Oil and Other Commodities: A Comparative Analysis

Listed author(s):
  • Abdullah, Ahmad Monir
  • Saiti, Buerhan
  • Masih, Abul Mansur M.

An understanding of how volatilities of and correlations between commodity returns change over time including their directions (positive or negative) and size (stronger or weaker) is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is a humble attempt to add value to the existing literature by empirically testing the ‘time-varying’ and ‘scale dependent’ volatilities of and correlations of the sample commodities. Particularly, by incorporating scale dependence, it is able to identify unique portfolio diversification opportunities for different set of investors bearing different investment horizons or holding periods. In order to address the research objectives, we have applied the vector error-correction test and several recently introduced econometric techniques such as the Maximum Overlap Discrete Wavelet Transform (MODWT), Continuous Wavelet Transform (CWT) and Multivariate GARCH – Dynamic Conditional Correlation. The data used in this paper is the daily data of seven commodities (crude oil, gas, gold, silver, copper, soybean and corn) prices from 1 January 2007 until 31 December 2013. Our findings tend to suggest that there is a theoretical relationship between the sample commodities (as evidenced in the cointegration tests) and that the oil, gold and corn variables are leading the other commodities (as evidenced in the Vector Error-Correction models). Consistent with these results, our analysis based on the application of the recent wavelet technique MODWT tends to indicate that the gold price return is leading the other commodities. From the point of view of portfolio diversification benefits based on the extent of dynamic correlations between variables, our results tend to suggest that an investor should be aware that the gas price return is less correlated with the crude oil in the short run (as evidenced in the continuous wavelet transform analysis), but due to its high volatility, it offsets its benefit of diversification in the long run and that an investor holding the crude oil can gain by including corn in his/her portfolio (as evidenced in the Dynamic conditional correlations analysis). Our analysis based on the recent applications of the wavelet decompositions and the dynamic conditional correlations helps us unveil the portfolio diversification opportunities for the investors with heterogeneous investment horizons or holding stocks over different periods.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 56988.

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Date of creation: 29 Jun 2014
Handle: RePEc:pra:mprapa:56988
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  1. Saghaian, Sayed H., 2010. "The Impact of the Oil Sector on Commodity Prices: Correlation or Causation?," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 42(03), pages 477-485, August.
  2. Saiti, Buerhan & Bacha, Obiyathulla & Masih, Mansur, 2014. "Testing the Conventional and Islamic Financial Market Contagion: Evidence from Wavelet Analysis," MPRA Paper 56907, University Library of Munich, Germany.
  3. D. S. Prasada Rao & Bart van Ark, 2013. "Introduction," Chapters,in: World Economic Performance, chapter 1, pages 1-6 Edward Elgar Publishing.
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  6. Andrew C. Worthington & Mosayeb Pahlavani, 2007. "Gold investment as an inflationary hedge: cointegration evidence with allowance for endogenous structural breaks," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 3(4), pages 259-262.
  7. Baffes, John, 2007. "Oil spills on other commodities," Resources Policy, Elsevier, vol. 32(3), pages 126-134, September.
  8. Mara Madaleno & Carlos Pinho, 2012. "International stock market indices comovements: a new look," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 89-102, 01.
  9. Natanelov, Valeri & Alam, Mohammad J. & McKenzie, Andrew M. & Van Huylenbroeck, Guido, 2011. "Is there co-movement of agricultural commodities futures prices and crude oil?," Energy Policy, Elsevier, vol. 39(9), pages 4971-4984, September.
  10. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-1189, December.
  11. Ke Tang & Wei Xiong, 2010. "Index Investment and Financialization of Commodities," NBER Working Papers 16385, National Bureau of Economic Research, Inc.
  12. Gallegati, Marco, 2008. "Wavelet analysis of stock returns and aggregate economic activity," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3061-3074, February.
  13. Mansur Masih & Ali Al-Elg & Haider Madani, 2009. "Causality between financial development and economic growth: an application of vector error correction and variance decomposition methods to Saudi Arabia," Applied Economics, Taylor & Francis Journals, vol. 41(13), pages 1691-1699.
  14. Natanelov, Valeri & Alam, Mohammad Jahangir & McKenzie, Andrew M. & Van Huylenbroeck, Guido, 2011. "Is There Co-Movement of Agricultural Commodities Futures Prices and Crude Oil?," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114626, European Association of Agricultural Economists.
  15. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "The crude oil market and the gold market: Evidence for cointegration, causality and price discovery," Resources Policy, Elsevier, vol. 35(3), pages 168-177, September.
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