IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v77y2022ics0301420722001829.html
   My bibliography  Save this article

The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function

Author

Listed:
  • Wang, Jianzhou
  • Niu, Xinsong
  • Zhang, Lifang
  • Liu, Zhenkun
  • Wei, Danxiang

Abstract

Achieving accurate exchange rate forecasts has a significant impact in construction of international trade and currency markets. However, because of the volatility of exchange rate series, accurate exchange rate prediction is still a difficult issue. In prior studies, researchers tend to conduct prediction research on individual variables of the real exchange rate and ignore the direct influence of other relevant economic factors on the real exchange rate forecasts, which leads to unsatisfactory prediction accuracy. At present, oil price shocks are often used as the dominant factor to explain the actual exchange rate behavior, and the analysis of the relationship between the two has become a hot issue. To explore the direct impact of oil prices on the real effective exchange rate forecast, a bivariate scheme is proposed, proving the important effect of oil price variable on exchange rate forecasting. The framework of this article starts from two aspects. First, several Copula functions are used to study the relationship between the two sequences, and the basic Copula functions including Clayton, Gumbel, and Frank functions are selected, and the three Copula functions are employed to obtain a hybrid Copula function using the improved Dragonfly optimization strategy. Next, a binary forecasting framework is constructed and a data preprocessing method is added to construct a forecasting model. Finally, this article demonstrates that the bivariate scheme achieves better forecasting capabilities than the univariate forecasting frame.

Suggested Citation

  • Wang, Jianzhou & Niu, Xinsong & Zhang, Lifang & Liu, Zhenkun & Wei, Danxiang, 2022. "The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function," Resources Policy, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001829
    DOI: 10.1016/j.resourpol.2022.102734
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420722001829
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2022.102734?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. Emrah Önder & Fɪrat Bayɪr & Ali Hepșen, 2013. "Forecasting Macroeconomic Variables using Artificial Neural Network and Traditional Smoothing Techniques," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 3(4), pages 1-6.
    2. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    3. De Vita, Glauco & Trachanas, Emmanouil, 2016. "‘Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India’ — A failed replication (negative Type 1 and Type 2)," Energy Economics, Elsevier, vol. 56(C), pages 150-160.
    4. Christiane Baumeister & Lutz Kilian, 2016. "Understanding the Decline in the Price of Oil since June 2014," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(1), pages 131-158.
    5. Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
    6. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    7. Wang, Jianzhou & Niu, Xinsong & Zhang, Linyue & Lv, Mengzheng, 2021. "Point and interval prediction for non-ferrous metals based on a hybrid prediction framework," Resources Policy, Elsevier, vol. 73(C).
    8. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    9. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
    10. Zhang, Xiaobo & Wang, Jianzhou & Gao, Yuyang, 2019. "A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM," Energy Economics, Elsevier, vol. 81(C), pages 899-913.
    11. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    12. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    13. Baghestani, Hamid & Chazi, Abdelaziz & Khallaf, Ashraf, 2019. "A directional analysis of oil prices and real exchange rates in BRIC countries," Research in International Business and Finance, Elsevier, vol. 50(C), pages 450-456.
    14. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    15. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    16. Du, Limin & Yanan, He & Wei, Chu, 2010. "The relationship between oil price shocks and China's macro-economy: An empirical analysis," Energy Policy, Elsevier, vol. 38(8), pages 4142-4151, August.
    17. Gao, Yuyang & Wang, Jianzhou & Yang, Hufang, 2022. "A multi-component hybrid system based on predictability recognition and modified multi-objective optimization for ultra-short-term onshore wind speed forecasting," Renewable Energy, Elsevier, vol. 188(C), pages 384-401.
    18. Guzman, Martin & Ocampo, Jose Antonio & Stiglitz, Joseph E., 2018. "Real exchange rate policies for economic development," World Development, Elsevier, vol. 110(C), pages 51-62.
    19. Baghestani, Hamid & Toledo, Hugo, 2019. "Oil prices and real exchange rates in the NAFTA region," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 253-264.
    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. Taufeeque Ahmad Siddiqui & Haseen Ahmed & Mohammad Naushad & Uzma Khan, 2023. "The Relationship between Oil Prices and Exchange Rate: A Systematic Literature Review," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 566-578, May.
    2. Huang, Menghao & Shao, Wei & Wang, Jian, 2023. "Correlations between the crude oil market and capital markets under the Russia–Ukraine conflict: A perspective of crude oil importing and exporting countries," Resources Policy, Elsevier, vol. 80(C).
    3. Zhang, Dongxue & Wang, Shuai & Liang, Yuqiu & Du, Zhiyuan, 2023. "A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer," Energy, Elsevier, vol. 264(C).
    4. Nikita D. Fokin & Ekaterina V. Malikova & Andrey V. Polbin, 2024. "Time-varying parameters error correction model for real ruble exchange rate and oil prices: What has changed due to capital control and sanctions?," Russian Journal of Economics, ARPHA Platform, vol. 10(1), pages 20-33, March.
    5. Niu, Xinsong & Wang, Jiyang & Wei, Danxiang & Zhang, Lifang, 2022. "A combined forecasting framework including point prediction and interval prediction for carbon emission trading prices," Renewable Energy, Elsevier, vol. 201(P1), pages 46-59.
    6. Sokhanvar, Amin & Çiftçioğlu, Serhan & Lee, Chien-Chiang, 2023. "The effect of energy price shocks on commodity currencies during the war in Ukraine," Resources Policy, Elsevier, vol. 82(C).
    7. Nie, Ying & Li, Ping & Wang, Jianzhou & Zhang, Lifang, 2024. "A novel multivariate electrical price bi-forecasting system based on deep learning, a multi-input multi-output structure and an operator combination mechanism," Applied Energy, Elsevier, vol. 366(C).
    8. Nakorji Musa & Oji-okoro Izuchukwu & Seyi Saint Akadiri, 2024. "Assessment of exchange rate determination in a mono-resource economy: A case of Nigeria," Journal of Economic Analysis, Anser Press, vol. 3(2), pages 101-120, June.
    9. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Lu, Haiyan & Zhang, Linyue, 2022. "A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory," Resources Policy, Elsevier, vol. 79(C).
    10. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    11. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    12. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
    13. Yang, Yi & Xing, Qianyi & Wang, Kang & Li, Caihong & Wang, Jianzhou & Huang, Xiaojia, 2024. "A novel combined probabilistic load forecasting system integrating hybrid quantile regression and knee improved multi-objective optimization strategy," Applied Energy, Elsevier, vol. 356(C).
    14. Amin Sokhanvar & Chien-Chiang Lee, 2023. "How do energy price hikes affect exchange rates during the war in Ukraine?," Empirical Economics, Springer, vol. 64(5), pages 2151-2164, May.

    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. Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
    2. Baghestani, Hamid & Chazi, Abdelaziz & Khallaf, Ashraf, 2019. "A directional analysis of oil prices and real exchange rates in BRIC countries," Research in International Business and Finance, Elsevier, vol. 50(C), pages 450-456.
    3. Wang, Jianzhou & Wang, Shuai & Li, Zhiwu, 2021. "Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression," Renewable Energy, Elsevier, vol. 179(C), pages 1246-1261.
    4. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    5. Wang, Jianzhou & Niu, Xinsong & Zhang, Linyue & Lv, Mengzheng, 2021. "Point and interval prediction for non-ferrous metals based on a hybrid prediction framework," Resources Policy, Elsevier, vol. 73(C).
    6. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
    7. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    8. Zhang, Guowei & Zhang, Yi & Wang, Hui & Liu, Da & Cheng, Runkun & Yang, Di, 2024. "Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network," Energy, Elsevier, vol. 288(C).
    9. Yang, Yi & Xing, Qianyi & Wang, Kang & Li, Caihong & Wang, Jianzhou & Huang, Xiaojia, 2024. "A novel combined probabilistic load forecasting system integrating hybrid quantile regression and knee improved multi-objective optimization strategy," Applied Energy, Elsevier, vol. 356(C).
    10. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    11. Brueckner, Marcus & Hong, Haidi & Vespignani, Joaquin, 2023. "Regulation of petrol and diesel prices and their effects on GDP growth: evidence from China," Working Papers 2023-02, University of Tasmania, Tasmanian School of Business and Economics.
    12. Jiang, Wei & Liu, Yan, 2021. "The asymmetric effect of crude oil prices on stock prices in major international financial markets," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    13. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    14. Nie, Ying & Liang, Ni & Wang, Jianzhou, 2021. "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, Elsevier, vol. 301(C).
    15. Liu, Donghui & Meng, Lingjie & Wang, Yudong, 2021. "The asymmetric effects of oil price changes on China’s exports: New evidence from a nonlinear autoregressive distributed lag model," Journal of Asian Economics, Elsevier, vol. 77(C).
    16. Takuji Fueki & Jouchi Nakajima & Shinsuke Ohyama & Yoichiro Tamanyu, 2021. "Identifying oil price shocks and their consequences: The role of expectations in the crude oil market," International Finance, Wiley Blackwell, vol. 24(1), pages 53-76, April.
    17. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    18. Zhang, Dongxue & Wang, Shuai & Liang, Yuqiu & Du, Zhiyuan, 2023. "A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer," Energy, Elsevier, vol. 264(C).
    19. Comincioli, Nicola & Hagspiel, Verena & Kort, Peter M. & Menoncin, Francesco & Miniaci, Raffaele & Vergalli, Sergio, 2021. "Mothballing in a Duopoly: Evidence from a (Shale) Oil Market," Energy Economics, Elsevier, vol. 104(C).
    20. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).

    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:77:y:2022:i:c:s0301420722001829. 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.