IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v63y2019ic20.html
   My bibliography  Save this article

Energy and non-energy commodities: An asymmetric approach towards portfolio diversification in the commodity market

Author

Listed:
  • Rehman, Mobeen Ur
  • Bouri, Elie
  • Eraslan, Veysel
  • Kumar, Satish

Abstract

We investigate the presence of short- and long-run asymmetric relationships between energy and non-energy commodities for weekly data from January 2010 to June 2018. Using the non-linear ARDL methodology, we show that oil prices have significant long-run negative effects on the prices of gold and silver, indicating that for both metal commodities, oil price increases lead to more decrease than the subsequent oil prices decrease. Crude oil, among other energy commodities, is found to offer more diversification benefits when combined with gold or silver; however minimal diversification benefits can result from combining crude oil with wheat or platinum. Gas futures, among other energy markets, offer more diversification opportunities when combined with copper, wheat, platinum or palladium, while coal offers maximum diversification benefits when combined with gold, silver or wheat. Our results are robust when NARDL is applied to the daily data and also when the analysis is conducted using the causality-in-quantiles test.

Suggested Citation

  • Rehman, Mobeen Ur & Bouri, Elie & Eraslan, Veysel & Kumar, Satish, 2019. "Energy and non-energy commodities: An asymmetric approach towards portfolio diversification in the commodity market," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  • Handle: RePEc:eee:jrpoli:v:63:y:2019:i:c:20
    DOI: 10.1016/j.resourpol.2019.101456
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420719303915
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.resourpol.2019.101456?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Gogolin, Fabian & Kearney, Fearghal & Lucey, Brian M. & Peat, Maurice & Vigne, Samuel A., 2018. "Uncovering long term relationships between oil prices and the economy: A time-varying cointegration analysis," Energy Economics, Elsevier, vol. 76(C), pages 584-593.
    3. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    4. Bouri, Elie & Lien, Donald & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Directional predictability of implied volatility: From crude oil to developed and emerging stock markets," Finance Research Letters, Elsevier, vol. 27(C), pages 65-79.
    5. Fousekis, Panos & Katrakilidis, Constantinos & Trachanas, Emmanouil, 2016. "Vertical price transmission in the US beef sector: Evidence from the nonlinear ARDL model," Economic Modelling, Elsevier, vol. 52(PB), pages 499-506.
    6. Lahiani, Amine & Hammoudeh, Shawkat & Gupta, Rangan, 2016. "Linkages between financial sector CDS spreads and macroeconomic influence in a nonlinear setting," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 443-456.
    7. López Cabrera, Brenda & Schulz, Franziska, 2016. "Volatility linkages between energy and agricultural commodity prices," Energy Economics, Elsevier, vol. 54(C), pages 190-203.
    8. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    9. Bouri, Elie & Gupta, Rangan & Lahiani, Amine & Shahbaz, Muhammad, 2018. "Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices," Resources Policy, Elsevier, vol. 57(C), pages 224-235.
    10. Peter Romilly & Haiyan Song & Xiaming Liu, 2001. "Car ownership and use in Britain: a comparison of the empirical results of alternative cointegration estimation methods and forecasts," Applied Economics, Taylor & Francis Journals, vol. 33(14), pages 1803-1818.
    11. Algieri, Bernardina & Leccadito, Arturo, 2017. "Assessing contagion risk from energy and non-energy commodity markets," Energy Economics, Elsevier, vol. 62(C), pages 312-322.
    12. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    13. Paresh Kumar Narayan & Stephan Popp, 2013. "Size and power properties of structural break unit root tests," Applied Economics, Taylor & Francis Journals, vol. 45(6), pages 721-728, February.
    14. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    15. Hall, Alastair R, 1994. "Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 461-470, October.
    16. Balcilar, Mehmet & Gupta, Rangan & Pierdzioch, Christian, 2016. "Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test," Resources Policy, Elsevier, vol. 49(C), pages 74-80.
    17. Dutta, Anupam & Bouri, Elie & Roubaud, David, 2019. "Nonlinear relationships amongst the implied volatilities of crude oil and precious metals," Resources Policy, Elsevier, vol. 61(C), pages 473-478.
    18. Anindya Banerjee & Juan Dolado & Ricardo Mestre, 1998. "Error‐correction Mechanism Tests for Cointegration in a Single‐equation Framework," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(3), pages 267-283, May.
    19. Kumar, Satish, 2019. "Asymmetric impact of oil prices on exchange rate and stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 41-51.
    20. Chattopadhyay, Manojit & Kumar Mitra, Subrata, 2015. "Exploring asymmetric behavior pattern from Indian oil products prices using NARDL and GHSOM approaches," Energy Policy, Elsevier, vol. 86(C), pages 262-272.
    21. Nazlioglu, Saban & Soytas, Ugur, 2011. "World oil prices and agricultural commodity prices: Evidence from an emerging market," Energy Economics, Elsevier, vol. 33(3), pages 488-496, May.
    22. Soytas, Ugur & Sari, Ramazan & Hammoudeh, Shawkat & Hacihasanoglu, Erk, 2009. "World oil prices, precious metal prices and macroeconomy in Turkey," Energy Policy, Elsevier, vol. 37(12), pages 5557-5566, December.
    23. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    24. Paresh Kumar Narayan & Stephan Popp, 2010. "A new unit root test with two structural breaks in level and slope at unknown time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1425-1438.
    25. Kumar, Satish, 2017. "On the nonlinear relation between crude oil and gold," Resources Policy, Elsevier, vol. 51(C), pages 219-224.
    26. Nusair, Salah A., 2016. "The effects of oil price shocks on the economies of the Gulf Co-operation Council countries: Nonlinear analysis," Energy Policy, Elsevier, vol. 91(C), pages 256-267.
    27. Yahya, Muhammad & Oglend, Atle & Dahl, Roy Endré, 2019. "Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach," Energy Economics, Elsevier, vol. 80(C), pages 277-296.
    28. Rehman, Mobeen Ur & Shahzad, Syed Jawad Hussain & Uddin, Gazi Salah & Hedström, Axel, 2018. "Precious metal returns and oil shocks: A time varying connectedness approach," Resources Policy, Elsevier, vol. 58(C), pages 77-89.
    29. 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.
    30. Shahzad, Syed Jawad Hussain & Nor, Safwan Mohd & Ferrer, Roman & Hammoudeh, Shawkat, 2017. "Asymmetric determinants of CDS spreads: U.S. industry-level evidence through the NARDL approach," Economic Modelling, Elsevier, vol. 60(C), pages 211-230.
    31. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    32. Anupam Dutta & Md Hasib Noor, 2017. "Oil and non-energy commodity markets: An empirical analysis of volatility spillovers and hedging effectiveness," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1324555-132, January.
    33. Reboredo, Juan C., 2013. "Is gold a hedge or safe haven against oil price movements?," Resources Policy, Elsevier, vol. 38(2), pages 130-137.
    34. Ji, Qiang & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2019. "Information interdependence among energy, cryptocurrency and major commodity markets," Energy Economics, Elsevier, vol. 81(C), pages 1042-1055.
    35. Nazlioglu, Saban & Soytas, Ugur, 2012. "Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis," Energy Economics, Elsevier, vol. 34(4), pages 1098-1104.
    Full references (including those not matched with items on IDEAS)

    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. Rehman, Mobeen Ur & Vinh Vo, Xuan, 2020. "Cryptocurrencies and precious metals: A closer look from diversification perspective," Resources Policy, Elsevier, vol. 66(C).
    2. Shahzad, Syed Jawad Hussain & Nor, Safwan Mohd & Ferrer, Roman & Hammoudeh, Shawkat, 2017. "Asymmetric determinants of CDS spreads: U.S. industry-level evidence through the NARDL approach," Economic Modelling, Elsevier, vol. 60(C), pages 211-230.
    3. Maitra, Debasish & Guhathakurta, Kousik & Kang, Sang Hoon, 2021. "The good, the bad and the ugly relation between oil and commodities: An analysis of asymmetric volatility connectedness and portfolio implications," Energy Economics, Elsevier, vol. 94(C).
    4. Eissa, Mohamad Abdelaziz & Al Refai, Hisham, 2019. "Modelling the symmetric and asymmetric relationships between oil prices and those of corn, barley, and rapeseed oil," Resources Policy, Elsevier, vol. 64(C).
    5. Naeem, Muhammad Abubakr & Hasan, Mudassar & Arif, Muhammad & Suleman, Muhammad Tahir & Kang, Sang Hoon, 2022. "Oil and gold as a hedge and safe-haven for metals and agricultural commodities with portfolio implications," Energy Economics, Elsevier, vol. 105(C).
    6. Albulescu, Claudiu Tiberiu & Tiwari, Aviral Kumar & Ji, Qiang, 2020. "Copula-based local dependence among energy, agriculture and metal commodities markets," Energy, Elsevier, vol. 202(C).
    7. Claudiu Albulescu & Aviral Tiwari & Qiang Ji, 2020. "Copula-based local dependence between energy, agriculture and metal commodity markets," Papers 2003.04007, arXiv.org.
    8. Cheng, Natalie Fang Ling & Hasanov, Akram Shavkatovich & Poon, Wai Ching & Bouri, Elie, 2023. "The US-China trade war and the volatility linkages between energy and agricultural commodities," Energy Economics, Elsevier, vol. 120(C).
    9. Shahzad, Farrukh & Bouri, Elie & Mokni, Khaled & Ajmi, Ahdi Noomen, 2021. "Energy, agriculture, and precious metals: Evidence from time-varying Granger causal relationships for both return and volatility," Resources Policy, Elsevier, vol. 74(C).
    10. Sergio Adriani David & Claudio M. C. Inácio & José A. Tenreiro Machado, 2019. "Ethanol Prices and Agricultural Commodities: An Investigation of Their Relationship," Mathematics, MDPI, vol. 7(9), pages 1-25, August.
    11. Yoon, Seong-Min, 2022. "On the interdependence between biofuel, fossil fuel and agricultural food prices: Evidence from quantile tests," Renewable Energy, Elsevier, vol. 199(C), pages 536-545.
    12. Wei Su, Chi & Wang, Xiao-Qing & Tao, Ran & Oana-Ramona, Lobonţ, 2019. "Do oil prices drive agricultural commodity prices? Further evidence in a global bio-energy context," Energy, Elsevier, vol. 172(C), pages 691-701.
    13. Yang, Dong-Xiao & Wu, Bi-Bo & Tong, Jing-Yang, 2021. "Dynamics and causality of oil price shocks on commodities: Quantile-on-quantile and causality-in-quantiles methods," Resources Policy, Elsevier, vol. 74(C).
    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. Dutta, Anupam & Bouri, Elie & Roubaud, David, 2019. "Nonlinear relationships amongst the implied volatilities of crude oil and precious metals," Resources Policy, Elsevier, vol. 61(C), pages 473-478.
    16. Muntasir Murshed & Mohamed Elheddad & Rizwan Ahmed & Mohga Bassim & Ei Thuzar Than, 2022. "Foreign Direct Investments, Renewable Electricity Output, and Ecological Footprints: Do Financial Globalization Facilitate Renewable Energy Transition and Environmental Welfare in Bangladesh?," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(1), pages 33-78, March.
    17. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
    18. Narayan, Paresh Kumar & Narayan, Seema & Mishra, Sagarika, 2013. "Has the structural break slowed down growth rates of stock markets?," Economic Modelling, Elsevier, vol. 30(C), pages 595-601.
    19. Rangan Gupta & Amine Lahiani & Chi-Chuan Lee & Chien-Chiang Lee, 2019. "Asymmetric dynamics of insurance premium: the impacts of output and economic policy uncertainty," Empirical Economics, Springer, vol. 57(6), pages 1959-1978, December.
    20. Aviral Tiwari & Muhammad Shahbaz, 2014. "Revisiting Purchasing Power Parity for India using threshold cointegration and nonlinear unit root test," Economic Change and Restructuring, Springer, vol. 47(2), pages 117-133, May.

    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:eee:jrpoli:v:63:y:2019:i:c:20. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    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.