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Global Macroeconomic Determinants of the Domestic Commodity Derivatives

In: Global Approaches in Financial Economics, Banking, and Finance

Author

Listed:
  • Cagatay Basarir

    (Bandirma Onyedi Eylul University)

  • Mehmet Fatih Bayramoglu

    (Bulent Ecevit University)

Abstract

Countries compete with products which have an absolute advantage in foreign trade operations. Also, there are derivative financial instruments derived from these products in many developing financial markets. Thus, these products provide opportunities for investors such as speculation, arbitrage, and particularly hedging with the help of trading in derivative markets. The trading of these products on derivative markets also brings about the impact of global parameters on spot markets, as well as on futures markets. Hence, it is important for both real investors and financial investors to determine and observe the major macroeconomic variables that affect these products. This chapter aims to determine macroeconomic variables which affect domestic (local) commodity derivatives such as banana (Central America and Ecuador), palm oil (Malaysia), rice (Thailand), and tea (Kenya). Thereby when the market efficiency is weak or almost absent, the ability to lower the fragility against risks faced by the investors and the other related parties by maintaining advance information is analyzed. For this purpose, K* (K Star) algorithm as a data mining method which is one of the knowledge-based analysis techniques is used in the analysis. In this chapter, four derivative products were estimated by the K* algorithm, which predicts whether their direction will decrease or increase during the next 18 months. The results show that the K* algorithm predicts an accuracy of 66.7–72.2% for three of the four domestic commodity derivatives so that this algorithm is successful in identifying similar properties between global macroeconomic variables and domestic commodity derivatives.

Suggested Citation

  • Cagatay Basarir & Mehmet Fatih Bayramoglu, 2018. "Global Macroeconomic Determinants of the Domestic Commodity Derivatives," Contributions to Economics, in: Hasan Dincer & Ümit Hacioglu & Serhat Yüksel (ed.), Global Approaches in Financial Economics, Banking, and Finance, chapter 0, pages 331-349, Springer.
    • Handle: RePEc:spr:conchp:978-3-319-78494-6_16
    DOI: 10.1007/978-3-319-78494-6_16
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    References listed on IDEAS

    as
    1. Anh Ngoc Lai & Constantin Mellios, 2016. "Valuation of commodity derivatives with an unobservable convenience yield," Post-Print hal-03708996, HAL.
    2. Dummu, Tata Rao, 2009. "Commodity Futures Markets in India: Its Impact on Production and Prices," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 64(3), pages 1-24.
    3. Barry K. Goodwin & Randy Schnepf, 2000. "Determinants of endogenous price risk in corn and wheat futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(8), pages 753-774, September.
    4. Brooks, Chris & Prokopczuk, Marcel & Wu, Yingying, 2013. "Commodity futures prices: More evidence on forecast power, risk premia and the theory of storage," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(1), pages 73-85.
    5. Arismendi, Juan C. & Back, Janis & Prokopczuk, Marcel & Paschke, Raphael & Rudolf, Markus, 2016. "Seasonal Stochastic Volatility: Implications for the pricing of commodity options," Journal of Banking & Finance, Elsevier, vol. 66(C), pages 53-65.
    6. 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.
    7. Jayati Ghosh, 2011. "Implications of regulating commodity derivatives markets in the USA and EU," PSL Quarterly Review, Economia civile, vol. 64(258), pages 287-304.
    8. Joseph, Anto & Sisodia, Garima & Tiwari, Aviral Kumar, 2014. "A frequency domain causality investigation between futures and spot prices of Indian commodity markets," Economic Modelling, Elsevier, vol. 40(C), pages 250-258.
    9. Anh Ngoc Lai & Constantin Mellios, 2016. "Valuation of commodity derivatives with an unobservable convenience yield," Post-Print halshs-01183166, HAL.
    10. repec:dau:papers:123456789/607 is not listed on IDEAS
    11. Miffre, Joëlle & Brooks, Chris, 2013. "Do long-short speculators destabilize commodity futures markets?," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 230-240.
    12. Jennifer Clapp & Eric Helleiner, 2012. "Troubled futures? The global food crisis and the politics of agricultural derivatives regulation," Review of International Political Economy, Taylor & Francis Journals, vol. 19(2), pages 181-207.
    13. Haase, Marco & Seiler Zimmermann, Yvonne & Zimmermann, Heinz, 2016. "The impact of speculation on commodity futures markets – A review of the findings of 100 empirical studies," Journal of Commodity Markets, Elsevier, vol. 3(1), pages 1-15.
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