Grey theory based evaluation of importers’ strategies for hedging the price risk in the Tanzanian oil supply chain: a focus on derivative products

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The landed cost for oil products in local markets is very often affected by the fluctuation of price related to the international purchasing from oil markets. As a result, oil products are primarily procured via term contracts i.e. derivatives as wholesalers are typically loath to rely heavily on spot supplies as these may be unreliable and exhibit high price volatility. In this study, we apply Grey Theory to evaluate the derivatives based strategies of the Tanzanian oil products imports for hedging the price risk in the local market. After comprehensive evaluation, we find that the applicability of oil derivatives by the Tanzanian importers is high. Thus, the government (i.e. Ministry of Finance and Planning, Ministry of Trade and Industry) and other stakeholders have the obligation to continue bringing awareness on the benefits of the derivative instruments in the purchasing of oil products, which ultimately upon application would bring a relief to all consumers of the oil products in the country. Moreover, as the grey theory can deal with vague and incomplete data, the proposed model can be applied as an evaluation tool for quantifying qualitative data in any industry.



  • Keywords

    Derivative Products; Grey Theory; Hedging; Oil Supply Chain; Price Risk.

  • References

      [1] Shaeri, J., Adaoglu, C. & Katircioglu, S.T. 2016. Oil price risk exposure: A comparison of financial and non-financial subsectors. Energy, Vol.109: 712-723.

      [2] Liu, C., Chen, J., Li, J. & Sun, X. 2016. Statistical properties of country risk ratings under oil price volatility: Evidence from selected oil-exporting countries. Energy Policy, Vol.92: 234-245.

      [3] Kakeu, J. & Bouaddi, M. 2017. Empirical evidence of news about future prospects in the risk pricing of oil assets. Energy Economics, Vol.64: 458-468.

      [4] Lux, T., Segnon, M. & Gupta, R. 2016. Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data. Energy Economics, Vol.56: 117-133.

      [5] Nazlioglu, S., Gormus, N.A. & Soytas, U. 2016. Oil prices and real estate investment trusts (REITs): Gradual-Shift causality and volatility transmission analysis. Energy Economics, Vol.60: 168-175.

      [6] Kristjanpoller, W. & Minutolo, M.C. 2016. Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems with Applications, Vol.65: 233-241.

      [7] Guo, Y., Wen, X., Wu, Y. & Guo, and X. 2016 How is China’s coke price related with the world oil price? The role of extreme movements. Economic Modelling, Vol.58: 22-33.

      [8] Pal, D. & Mitra, S.K. 2016. Asymmetric oil product pricing in India: Evidence from a multiple threshold nonlinear ARDL model. Economic Modelling, Vol.59: 314-328.

      [9] Ewing, B.T. & Malik, F. 2017. Modelling asymmetric volatility in oil prices under structural breaks. Energy Economics, Vol.63: 227-233.

      [10] Wang, J. & Wang, J. 2016. Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations. Energy, Vol.102: 365-374.

      [11] Tiwari, A.K. & Albulescu, C.T. 2016. Oil price and exchange rate in India: Fresh evidence from continuous wavelet approach and asymmetric, multi-horizon Granger-causality tests. Applied Energy, Vol.179: 272-283.

      [12] Ahmadi, M., Behmiri, N.B. & Manera, M. 2016. How is volatility in commodity markets linked to oil price shocks? Energy Economics, Vol.59: 11-23.

      [13] Mnasri, M., Dionne, G. & Gueyie, J.P. 2017. The use of nonlinear hedging strategies by US oil producers: Motivations and implications. Energy Economics, Vol. 63: 348-364.

      [14] Liu, L., Wang, Y., Wu, C. & Wu, W. 2016. Disentangling the determinants of real oil prices. Energy Economics, Vol.56: 363-373.

      [15] Zhang, J. & Xie, M. 2016. China’s oil product pricing mechanism: What role does it play in China’s macroeconomy? China Economic Review, Vol.38: 209-221.

      [16] Ji, Q., Fan,Y. & Geng, J.B. 2014. Separated influence of crude oil prices on regional natural gas import prices. Energy Policy, Vol.70: 96-105.

      [17] Chen, Y., Zou,Y., Zhou,Y. & Zhang, C. 2016. Multi-step-ahead crude oil price-Forecasting based on Grey Wave Forecasting Method. Procedia Computer Science, Vol. 91: 1050-1056.

      [18] Guay, W.R. 1999. The impact of derivatives on firm risk: An empirical examination of new derivative users. Journal of Accounting and Economics, Vol.26 (1-3): 319-351.

      [19] Ma, J., Liu,J., Huang, D. & Chen, W. 2017. Forecasting the oil futures price volatility: A new approach. Economic Modelling, Vol.64: 560-566.

      [20] Phan, D., Nguyen,H. & Faff, R. 2014. Uncovering the asymmetric linkage between financial derivatives and firm value – The case of oil and gas exploration and production companies. Energy Economics, Vol.45: 340-352.

      [21] Turner, P.A. & Lim. S.H. 2015. Hedging jet fuel price risk: The case of U.S. passenger airlines. Journal of Air Transport Management, Vol.44-45: 54-64.

      [22] Yu, J., Zhang, X. & Xiong, C. 2017. A methodology for evaluating micro-surfacing treatment on asphalt pavement based on grey system models and grey rational degree theory. Construction and Building Materials, Vol.150: 214-226.

      [23] Zhicheng, Y., Lijun, W., Zhaokuo, Y. & Haowen, L. 2017. Shape optimization of welded plate heat exchangers based on grey correlation theory. Applied Thermal Engineering, Vol.123: 761-769.

      [24] Rajeswari, B. & Amirthagadeswaran, K.S. 2017. Experimental investigation of machinability characteristics and multi-response optimization of end milling in aluminium composites using RSM based grey relational analysis. Measurement, Vol. 105: 78-86.

      [25] Baruah, A., Pandivelan, C. & Jeevanantham, A.K. 2017. Optimization of AA5052 in incremental sheet forming using grey relational analysis. Measurement, Vol. 106: 95-100.

      [26] Mathivathanan, D., Govindan, K. & Haq, A.N. 2017. Exploring the impact of dynamic capabilities on sustainable supply chain firm’s performance using Grey-Analytical Hierarchy Process. Journal of Cleaner Production, Vol. 147: 637-653.

      [27] Thakur, V. & Ramesh, A. 2015. Selection of Waste Disposal Firms Using Grey Theory Based Multi-criteria Decision Making Technique. Procedia-Social and Behavioral Sciences, Vol.189: 81-90.

      [28] Wei, J., Zhou, L., Wang, F. & Wu, D. 2015. Work safety evaluation in Mainland China using grey theory. Applied Mathematical Modelling, Vol. 39(2): 924-933.

      [29] Yan, F., Qiao, D., Qian, B., Ma, L., Xing, X., Zhang, Y. and Wang, X. 2016. Improvement of CCME WQI using grey relational method. Journal of Hydrology, Vol.543 (B): 316-323.

      [30] Golinska, P., Kosacka, M., Mierzwiak, R. & Werner-Lewandowska, K. 2015. Grey Decision Making as a tool for the classification of the sustainability level of remanufacturing companies. Journal of Cleaner Production, Vol. 105: 28-40.

      [31] Chithambaranathan, P., Subramanian, N., Gunasekaran, A. & Palaniappan, P.L.K. 2015. Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. International Journal of Production Economics, Vol. 166: 163-176.

      [32] Rajesh, R. & Ravi, V. 2015. Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, Vol.86: 343-359.

      [33] Baskaran, V., Nachiappan, S. & Rahman, S. 2012. Indian textile suppliers’ sustainability evaluation using the grey approach. International Journal of Production Economics, Vol.135 (2): 647-658.

      [34] Celikbilek, Y. & Tüysüz, F. 2016. An integrated grey based multi-criteria decision-making approach for the evaluation of renewage energy sources. Energy, Vol.115 (Part 1): 1246-1258.

      [35] Deng, J.L. 1982. The introduction of grey system. The Journal of Grey System, 1(1): 1-24.

      [36] Yang, Y, and John, R. 2012. Grey sets and greyness. Inform. Sci., 185: 249-264.

      [37] Liu, S., Fang, Z., Yang, Y. & Forrest, J. 2012. General grey numbers and their operations, Grey systems: Theory and Application, 2(3): 341-349.

      [38] Massami, E.P. & Myamba, B.M. 2016. Application of Vague Analytical Hierarchy Process to Prioritize the Challenges Facing Public Transportation in Dar Es Salaam City - Tanzania. International Journal of Advanced Research in Artificial Intelligence, Vol. 5(3): 46-53.




Article ID: 12196
DOI: 10.14419/ijamr.v7i3.12196

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.