IDEAS home Printed from https://ideas.repec.org/h/spr/conchp/978-3-319-78494-6_16.html
   My bibliography  Save this book chapter

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. repec:dau:papers:123456789/607 is not listed on IDEAS
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Anh Ngoc Lai & Constantin Mellios, 2016. "Valuation of commodity derivatives with an unobservable convenience yield," Post-Print hal-03708996, HAL.
    10. 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.
    11. 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.
    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. Anh Ngoc Lai & Constantin Mellios, 2016. "Valuation of commodity derivatives with an unobservable convenience yield," Post-Print halshs-01183166, HAL.
    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. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    2. Ma, Zonggang & Ma, Chaoqun & Wu, Zhijian, 2020. "Closed-form analytical solutions for options on agricultural futures with seasonality and stochastic convenience yield," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    3. Fizaine, Florian, 2018. "Toward generalization of futures contracts for raw materials: A probabilistic answer applied to metal markets," Resources Policy, Elsevier, vol. 59(C), pages 379-388.
    4. Gonzato, Luca & Sgarra, Carlo, 2021. "Self-exciting jumps in the oil market: Bayesian estimation and dynamic hedging," Energy Economics, Elsevier, vol. 99(C).
    5. Algieri, Bernardina & Leccadito, Arturo, 2019. "Price volatility and speculative activities in futures commodity markets: A combination of combinations of p-values test," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 40-54.
    6. Roncoroni, Andrea & Prokopczuk, Marcel & Ronn, Ehud I., 2018. "Introduction—special issue on commodity and energy markets in the Journal of Banking and Finance," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 1-4.
    7. Andreasson, Pierre & Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah, 2016. "Impact of speculation and economic uncertainty on commodity markets," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 115-127.
    8. Sarveshwar Kumar Inani, 2018. "Price Discovery and Efficiency of Indian Agricultural Commodity Futures Market: An Empirical Investigation," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(1), pages 129-154, March.
    9. Zonggang Ma & Chaoqun Ma & Zhijian Wu, 2022. "Pricing commodity-linked bonds with stochastic convenience yield, interest rate and counterparty credit risk: application of Mellin transform methods," Review of Derivatives Research, Springer, vol. 25(1), pages 47-91, April.
    10. Go, You-How & Lau, Wee-Yeap, 2017. "Investor demand, market efficiency and spot-futures relation: Further evidence from crude palm oil," Resources Policy, Elsevier, vol. 53(C), pages 135-146.
    11. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
    12. Darren Shannon & Grigorios Fountas, 2021. "Extending the Heston Model to Forecast Motor Vehicle Collision Rates," Papers 2104.11461, arXiv.org, revised May 2021.
    13. Mohcine Bakhat & Klaas WŸrzburg, 2013. "Co-integration of Oil and Commodity Prices: A Comprehensive ApproachAbstract," Working Papers fa05-2013, Economics for Energy.
    14. Karakotsios, Achillefs & Katrakilidis, Constantinos & Kroupis, Nikolaos, 2021. "The dynamic linkages between food prices and oil prices. Does asymmetry matter?," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    15. Jilong Chen & Christian Ewald & Ruolan Ouyang & Sjur Westgaard & Xiaoxia Xiao, 2022. "Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil," Annals of Operations Research, Springer, vol. 313(1), pages 29-46, June.
    16. Natanelov, Valeri & McKenzie, Andrew M. & Van Huylenbroeck, Guido, 2013. "Crude oil–corn–ethanol – nexus: A contextual approach," Energy Policy, Elsevier, vol. 63(C), pages 504-513.
    17. Carlotta Penone & Elisa Giampietri & Samuele Trestini, 2022. "Futures–spot price transmission in EU corn markets," Agribusiness, John Wiley & Sons, Ltd., vol. 38(3), pages 679-709, July.
    18. Stefano Di Bucchianico, 2020. "A note on financialization from a Classical-Keynesian standpoint," Department of Economics University of Siena 824, Department of Economics, University of Siena.
    19. Carlotta Penone & Samuele Trestini, 2022. "Testing for asymmetric cointegration of Italian agricultural commodities prices: Evidence from the futures-spot market relationship," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(2), pages 50-58.
    20. Matteo Manera & Marcella Nicolini & Ilaria Vignati, 2012. "Returns in commodities futures markets and financial speculation: a multivariate GARCH approach," Quaderni di Dipartimento 170, University of Pavia, Department of Economics and Quantitative Methods.

    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:spr:conchp:978-3-319-78494-6_16. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.