IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/56988.html
   My bibliography  Save this paper

Diversification in Crude Oil and Other Commodities: A Comparative Analysis

Author

Listed:
  • Abdullah, Ahmad Monir
  • Saiti, Buerhan
  • Masih, Abul Mansur M.

Abstract

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.

Suggested Citation

  • Abdullah, Ahmad Monir & Saiti, Buerhan & Masih, Abul Mansur M., 2014. "Diversification in Crude Oil and Other Commodities: A Comparative Analysis," MPRA Paper 56988, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:56988
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/56988/1/MPRA_paper_56988.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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(3), pages 477-485, August.
    2. Vacha, Lukas & Barunik, Jozef, 2012. "Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis," Energy Economics, Elsevier, vol. 34(1), pages 241-247.
    3. Baffes, John, 2007. "Oil spills on other commodities," Resources Policy, Elsevier, vol. 32(3), pages 126-134, September.
    4. 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.
    5. Ke Tang & Wei Xiong, 2012. "Index Investment and the Financialization of Commodities," Financial Analysts Journal, Taylor & Francis Journals, vol. 68(6), pages 54-74, November.
    6. Gallegati, Marco, 2008. "Wavelet analysis of stock returns and aggregate economic activity," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3061-3074, February.
    7. 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.
    8. Buerhan Saiti & Obiyathulla Ismath Bacha & Mansur Masih, 2016. "Testing the Conventional and Islamic Financial Market Contagion: Evidence from Wavelet Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(8), pages 1832-1849, August.
    9. Jammazi, Rania & Aloui, Chaker, 2010. "Wavelet decomposition and regime shifts: Assessing the effects of crude oil shocks on stock market returns," Energy Policy, Elsevier, vol. 38(3), pages 1415-1435, March.
    10. Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon, 2001. "Differentiating intraday seasonalities through wavelet multi-scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(3), pages 543-556.
    11. 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, January.
    12. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Manuel Monge & Luis A. Gil-Alana, 2020. "The Lithium Industry and Analysis of the Beta Term Structure of Oil Companies," Risks, MDPI, vol. 8(4), pages 1-17, December.
    2. Shelly Singhal & Pratap Chandra Biswal, 2021. "Dynamic Commodity Portfolio Management: A Regime-switching VAR Model," Global Business Review, International Management Institute, vol. 22(2), pages 532-549, April.
    3. Tiwari, Aviral Kumar & Nasreen, Samia & Shahbaz, Muhammad & Hammoudeh, Shawkat, 2020. "Time-frequency causality and connectedness between international prices of energy, food, industry, agriculture and metals," Energy Economics, Elsevier, vol. 85(C).
    4. Tetsuji Tanaka & Jin Guo, 2020. "International price volatility transmission and structural change: a market connectivity analysis in the beef sector," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abdullah, Ahmad Monir & Saiti, Buerhan & Masih, Abul Mansur M., 2014. "The Impact of Crude Oil Price on Islamic Stock Indices of South East Asian (SEA) Countries: A Comparative Analysis," MPRA Paper 56957, University Library of Munich, Germany.
    2. Amar, Amine Ben & Goutte, Stéphane & Isleimeyyeh, Mohammad & Benkraiem, Ramzi, 2022. "Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    3. Rithuan, Syahidah Hanis Meor & Abdullah, Ahmad Monir & Masih, Abul Mansur M., 2014. "The Impact of Crude Oil Price on Islamic Stock Indices of Gulf Cooperation Council (GCC) Countries: A Comparative Analysis," MPRA Paper 56989, University Library of Munich, Germany.
    4. Rafiq, Shuddhasattwa & Bloch, Harry, 2016. "Explaining commodity prices through asymmetric oil shocks: Evidence from nonlinear models," Resources Policy, Elsevier, vol. 50(C), pages 34-48.
    5. Guo, Jin & Tanaka, Tetsuji, 2022. "Energy security versus food security: An analysis of fuel ethanol- related markets using the spillover index and partial wavelet coherence approaches," Energy Economics, Elsevier, vol. 112(C).
    6. Karel Janda & Ladislav Kristoufek, 2019. "The relationship between fuel and food prices: Methods, outcomes, and lessons for commodity price risk management," CAMA Working Papers 2019-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    8. Ali, Hakim & Masih, Mansur, 2016. "Evidence of cross-country portfolio diversification benefits: The case of Saudi Arabia," MPRA Paper 72180, University Library of Munich, Germany.
    9. Śmiech, Sławomir & Papież, Monika & Dąbrowski, Marek A., 2015. "Does the euro area macroeconomy affect global commodity prices? Evidence from a SVAR approach," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 485-503.
    10. Karel Janda & Ladislav Krištoufek, 2019. "The Relationship Between Fuel and Food Prices: Methods and Outcomes," Annual Review of Resource Economics, Annual Reviews, vol. 11(1), pages 195-216, October.
    11. Ben Amar, Amine & Goutte, Stéphane & Isleimeyyeh, Mohammad, 2022. "Asymmetric cyclical connectedness on the commodity markets: Further insights from bull and bear markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 386-400.
    12. Abdullah, Ahmad Monir & Masih, Abul Mansur M., 2014. "The Impact of Crude Oil Price on Macroeconomic Variables: New Evidence from Malaysia," MPRA Paper 56976, University Library of Munich, Germany.
    13. Umirah, Fatin & Masih, Mansur, 2017. "Should the Malaysian Islamic stock market investors invest in regional and international equity market to gain portfolio diversification benefits ?," MPRA Paper 79762, University Library of Munich, Germany.
    14. Cheng, Sheng & Cao, Yan, 2019. "On the relation between global food and crude oil prices: An empirical investigation in a nonlinear framework," Energy Economics, Elsevier, vol. 81(C), pages 422-432.
    15. Karoline Krätschell & Torsten Schmidt, 2017. "Long-run waves or short-run fluctuations – what establishes the correlation between oil and food prices?," Applied Economics, Taylor & Francis Journals, vol. 49(54), pages 5535-5546, November.
    16. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    17. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    18. Kumeka, Terver Theophilus & Uzoma-Nwosu, Damian Chidozie & David-Wayas, Maria Onyinye, 2022. "The effects of COVID-19 on the interrelationship among oil prices, stock prices and exchange rates in selected oil exporting economies," Resources Policy, Elsevier, vol. 77(C).
    19. Saiti, Buerhan & Bacha, Obiyathulla & Masih, Mansur, 2014. "Is the global leadership of the US financial market over other financial markets shaken by 2007-2009 financial crisis? Evidence from Wavelet Analysis," MPRA Paper 57064, University Library of Munich, Germany.
    20. Bhuiyan, Rubaiyat Ahsan & Rahman, Maya Puspa & Saiti, Buerhan & Ghani, Gairuzazmi Bin Mat, 2019. "Does the Malaysian Sovereign sukuk market offer portfolio diversification opportunities for global fixed-income investors? Evidence from wavelet coherence and multivariate-GARCH analyses," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 675-687.

    More about this item

    Keywords

    Commodity; MODWT; CWT; DCC-MGARCH; Diversification; Causality;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:56988. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.