IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v45y2015icp121-129.html
   My bibliography  Save this article

Decreasing minerals׳ revenue risk by diversification of mineral production in mineral rich countries

Author

Listed:
  • Adibi, Nabiollah
  • Ataee-pour, Majid

Abstract

There are numerous countries that have a variety of mineral products and commodities which are the source of government revenue and foreign exchange. Mineral revenues are essential for the development of mineral rich countries. Generally, the minerals market has many risks; therefore the revenue gained from minerals is accompanied with a level of risk. In order to plan for mineral revenue by governments, the risk of mineral revenue should be decreased. In this paper, minerals are considered as risky assets to apply the Modern Portfolio Theory (MPT). This method is able to decrease the risk of minerals׳ revenue by optimum diversification. As a case study, mineral production of Iran׳s mining sector is studied. The non-linear model is solved by quadratic programming. To achieve the country׳s expected revenue the model determines the type and quantity of minerals to be produced. The findings show that the MPT model has an adequate potential to reduce the risk of the government׳s mineral revenue compared with the current situation.

Suggested Citation

  • Adibi, Nabiollah & Ataee-pour, Majid, 2015. "Decreasing minerals׳ revenue risk by diversification of mineral production in mineral rich countries," Resources Policy, Elsevier, vol. 45(C), pages 121-129.
    • Handle: RePEc:eee:jrpoli:v:45:y:2015:i:c:p:121-129
    DOI: 10.1016/j.resourpol.2015.04.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420715000434
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2015.04.006?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. Arezki, Rabah & Dumitrescu, Elena & Freytag, Andreas & Quintyn, Marc, 2014. "Commodity prices and exchange rate volatility: Lessons from South Africa's capital account liberalization," Emerging Markets Review, Elsevier, vol. 19(C), pages 96-105.
    2. Yao, Haixiang & Li, Zhongfei & Chen, Shumin, 2014. "Continuous-time mean–variance portfolio selection with only risky assets," Economic Modelling, Elsevier, vol. 36(C), pages 244-251.
    3. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    4. Ahmed Mahmud & Syed Basher, 2014. "Price volatility and the political economy of resource-rich nations," Economics of Governance, Springer, vol. 15(3), pages 253-279, August.
    5. Cochran, Steven J. & Mansur, Iqbal & Odusami, Babatunde, 2012. "Volatility persistence in metal returns: A FIGARCH approach," Journal of Economics and Business, Elsevier, vol. 64(4), pages 287-305.
    6. Ioana Dorin & Cristina Diaconescu & Dan Ioan Topor, 2014. "The Role of Mining in National Economies," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(3), pages 155-160, July.
    7. Gil-Alana, Luis A. & Tripathy, Trilochan, 2014. "Modelling volatility persistence and asymmetry: A Study on selected Indian non-ferrous metals markets," Resources Policy, Elsevier, vol. 41(C), pages 31-39.
    8. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    9. McMillan, David G. & Speight, Alan E. H., 2001. "Non-ferrous metals price volatility: a component analysis," Resources Policy, Elsevier, vol. 27(3), pages 199-207, September.
    10. Simon Benninga, 2008. "Financial Modeling, 3rd Edition," MIT Press Books, The MIT Press, edition 3, volume 1, number 0262026287, December.
    11. Ma, Yiqun, 2013. "Iron ore spot price volatility and change in forward pricing mechanism," Resources Policy, Elsevier, vol. 38(4), pages 621-627.
    12. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
    13. Brunetti, Celso & Gilbert, Christopher L., 1995. "Metals price volatility, 1972-1995," Resources Policy, Elsevier, vol. 21(4), pages 237-254, December.
    14. N. J. Jobst & M. D. Horniman & C. A. Lucas & G. Mitra, 2001. "Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints," Quantitative Finance, Taylor & Francis Journals, vol. 1(5), pages 489-501.
    15. Epaminondas Panas & Vassilia Ninni, 2010. "The Distribution of London Metal Exchange Prices: A Test of the Fractal Market Hypothesis," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 192-210.
    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. Chomba Kolala & Bridget Bwalya Umar, 2019. "National benefits, local costs? Local residents' views on environmental and social effects of large‐scale mining in Chingola, Zambia," Natural Resources Forum, Blackwell Publishing, vol. 43(4), pages 205-217, November.
    2. Jiang, Meihui & An, Haizhong & Guan, Qing & Sun, Xiaoqi, 2018. "Global embodied mineral flow between industrial sectors: A network perspective," Resources Policy, Elsevier, vol. 58(C), pages 192-201.
    3. Ren, Bo & Li, Huajiao & Wang, Xingxing & Shi, Jianglan & Ma, Ning & Qi, Yajie, 2022. "The flow of embodied minerals between China's provinces and the world: A nested supply chain network perspective," Resources Policy, Elsevier, vol. 78(C).

    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. Gil-Alana, Luis A. & Tripathy, Trilochan, 2014. "Modelling volatility persistence and asymmetry: A Study on selected Indian non-ferrous metals markets," Resources Policy, Elsevier, vol. 41(C), pages 31-39.
    2. Sinha, Pankaj & Mathur, Kritika, 2013. "A study on the Price Behavior of Base Metals traded in India," MPRA Paper 47028, University Library of Munich, Germany.
    3. Adewuyi, Adeolu O. & Wahab, Bashir A. & Adeboye, Olusegun S., 2020. "Stationarity of prices of precious and industrial metals using recent unit root methods: Implications for markets’ efficiency," Resources Policy, Elsevier, vol. 65(C).
    4. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2020. "Spillovers, integration and causality in LME non-ferrous metal markets," Journal of Commodity Markets, Elsevier, vol. 17(C).
    5. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The volatility effect on precious metals price returns in a stochastic volatility in mean model with time-varying parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    6. Berna Kirkulak-Uludag & Zorikto Lkhamazhapov, 2017. "Volatility Dynamics of Precious Metals: Evidence from Russia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(4), pages 300-317, August.
    7. Aikins Abakah, Emmanuel Joel & Gil-Alana, Luis A. & Tripathy, Trilochan, 2022. "Stochastic structure of metal prices: Evidence from fractional integration non-linearities and breaks," Resources Policy, Elsevier, vol. 78(C).
    8. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
    9. Zhang, Miao & Chen, Ping, 2016. "Mean–variance asset–liability management under constant elasticity of variance process," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 11-18.
    10. Zhang, Caibin & Liang, Zhibin, 2022. "Optimal time-consistent reinsurance and investment strategies for a jump–diffusion financial market without cash," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    11. Chen, Wei & Zhang, Wei-Guo, 2010. "The admissible portfolio selection problem with transaction costs and an improved PSO algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 2070-2076.
    12. repec:ipg:wpaper:2013-009 is not listed on IDEAS
    13. Amélie Charles & Olivier Darné & Jae H. Kim, 2014. "Precious metals shine? A market efficiency perspective," Working Papers hal-01010516, HAL.
    14. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2015. "Linear vs. quadratic portfolio selection models with hard real-world constraints," Computational Management Science, Springer, vol. 12(3), pages 345-370, July.
    15. Malinda & Maya & Jo-Hui & Chen, 2022. "Testing for the Long Memory and Multiple Structural Breaks in Consumer ETFs," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-6.
    16. Walter Briec & Kristiaan Kerstens & Octave Jokung, 2007. "Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach," Management Science, INFORMS, vol. 53(1), pages 135-149, January.
    17. Chen, Mei-Hsiu, 2010. "Understanding world metals prices--Returns, volatility and diversification," Resources Policy, Elsevier, vol. 35(3), pages 127-140, September.
    18. Zhang, Miao & Chen, Ping & Yao, Haixiang, 2017. "Mean-variance portfolio selection with only risky assets under regime switching," Economic Modelling, Elsevier, vol. 62(C), pages 35-42.
    19. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    20. Gil-Alana, Luis A. & Monge, Manuel, 2019. "Lithium: Production and estimated consumption. Evidence of persistence," Resources Policy, Elsevier, vol. 60(C), pages 198-202.
    21. 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.

    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:45:y:2015:i:c:p:121-129. 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.