IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v55y2021ics0275531920302002.html
   My bibliography  Save this article

Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network

Author

Listed:
  • Liu, Chang
  • Sun, Xiaolei
  • Wang, Jun
  • Li, Jianping
  • Chen, Jianming

Abstract

In the context of the growing financialisation of commodity markets, debate on how they interact with each other has returned to centre stage. The main motivation of this study is to research the price interactions of international commodities from the perspective of information transmission by proposing an innovative transfer entropy network based on empirical mode decomposition. We also identify core commodities with the strongest transmission intensity in information transmission networks at different time scales. The empirical results demonstrate that the network transmission structure and core varieties change based on the time scale. In the short term, metals have the strongest transmission intensity, whereas, in the medium and long term, the energy sector has the strongest transmission intensity. These findings should allow regulators and market participants to better understand the essential characteristics and internal structures of international commodity markets.

Suggested Citation

  • Liu, Chang & Sun, Xiaolei & Wang, Jun & Li, Jianping & Chen, Jianming, 2021. "Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network," Research in International Business and Finance, Elsevier, vol. 55(C).
    • Handle: RePEc:eee:riibaf:v:55:y:2021:i:c:s0275531920302002
    DOI: 10.1016/j.ribaf.2020.101318
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531920302002
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2020.101318?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. Büyükşahin, Bahattin & Robe, Michel A., 2014. "Speculators, commodities and cross-market linkages," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 38-70.
    2. David S. Jacks, 2019. "From boom to bust: a typology of real commodity prices in the long run," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 13(2), pages 201-220, May.
    3. Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.
    4. Vacha, Lukas & Barunik, Jozef, 2012. "Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis," Energy Economics, Elsevier, vol. 34(1), pages 241-247.
    5. Wei, Lu & Li, Guowen & Zhu, Xiaoqian & Sun, Xiaolei & Li, Jianping, 2019. "Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures," Energy Economics, Elsevier, vol. 80(C), pages 452-460.
    6. Sun, Xiaolei & Wang, Jun & Yao, Yanzhen & Li, Jingyu & Li, Jianping, 2020. "Spillovers among sovereign CDS, stock and commodity markets: A correlation network perspective," International Review of Financial Analysis, Elsevier, vol. 68(C).
    7. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    8. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    9. Hernandez, Jose Areola & Shahzad, Syed Jawad Hussain & Uddin, Gazi Salah & Kang, Sang Hoon, 2019. "Can agricultural and precious metal commodities diversify and hedge extreme downside and upside oil market risk? An extreme quantile approach," Resources Policy, Elsevier, vol. 62(C), pages 588-601.
    10. Yannick Le Pen & Benoît Sévi, 2013. "Futures trading and the excess comovement of commodity prices," Post-Print hal-01613916, HAL.
    11. Ji, Qiang & Li, Jianping & Sun, Xiaolei, 2019. "Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports," Finance Research Letters, Elsevier, vol. 30(C), pages 420-425.
    12. Anoop S Kumar & Taufeeq Ajaz, 2019. "Co-movement in crypto-currency markets: evidences from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-17, December.
    13. Pal, Debdatta & Mitra, Subrata K., 2017. "Time-frequency contained co-movement of crude oil and world food prices: A wavelet-based analysis," Energy Economics, Elsevier, vol. 62(C), pages 230-239.
    14. Ahmadi, Maryam & Bashiri Behmiri, Niaz & Manera, Matteo, 2016. "How is volatility in commodity markets linked to oil price shocks?," Energy Economics, Elsevier, vol. 59(C), pages 11-23.
    15. He, Jiayi & Shang, Pengjian, 2017. "Comparison of transfer entropy methods for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 772-785.
    16. Lescaroux, François, 2009. "On the excess co-movement of commodity prices--A note about the role of fundamental factors in short-run dynamics," Energy Policy, Elsevier, vol. 37(10), pages 3906-3913, October.
    17. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    18. Zhang, Dayong & Broadstock, David C., 2020. "Global financial crisis and rising connectedness in the international commodity markets," International Review of Financial Analysis, Elsevier, vol. 68(C).
    19. Boyd, Naomi E. & Harris, Jeffrey H. & Li, Bingxin, 2018. "An update on speculation and financialization in commodity markets," Journal of Commodity Markets, Elsevier, vol. 10(C), pages 91-104.
    20. Fernandez, Viviana, 2015. "Influence in commodity markets: Measuring co‐movement globally," Resources Policy, Elsevier, vol. 45(C), pages 151-164.
    21. Yan‐ran Ma & Qiang Ji & Jiaofeng Pan, 2019. "Oil financialization and volatility forecast: Evidence from multidimensional predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 564-581, September.
    22. Sun, Xiaolei & Yao, Xiaoyang & Wang, Jun, 2017. "Dynamic interaction between economic policy uncertainty and financial stress: A multi-scale correlation framework," Finance Research Letters, Elsevier, vol. 21(C), pages 214-221.
    23. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    24. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-1189, December.
    25. Shamaila Butt & Suresh Ramakrishnan & Nanthakumar Loganathan & Muhammad Ali Chohan, 2020. "Evaluating the exchange rate and commodity price nexus in Malaysia: evidence from the threshold cointegration approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.
    26. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2016. "Time–frequency featured co-movement between the stock and prices of crude oil and gold," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 985-995.
    27. Ohashi, Kazuhiko & Okimoto, Tatsuyoshi, 2016. "Increasing trends in the excess comovement of commodity prices," Journal of Commodity Markets, Elsevier, vol. 1(1), pages 48-64.
    28. Qiang Ji & Jianping Li & Xiaolei Sun, 2019. "New Challenge and Research Development in Global Energy Financialization," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(12), pages 2669-2672, September.
    29. Rafiq, Shuddhasattwa & Bloch, Harry, 2016. "Explaining commodity prices through asymmetric oil shocks: Evidence from nonlinear models," Resources Policy, Elsevier, vol. 50(C), pages 34-48.
    30. Sun, Xiaolei & Liu, Chang & Wang, Jun & Li, Jianping, 2020. "Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    31. Hu, Min & Zhang, Dayong & Ji, Qiang & Wei, Lijian, 2020. "Macro factors and the realized volatility of commodities: A dynamic network analysis," Resources Policy, Elsevier, vol. 68(C).
    32. Xiuwen Chen & Xiaolei Sun & Jianping Li, 2020. "How does economic policy uncertainty react to oil price shocks? A multi-scale perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 27(3), pages 188-193, February.
    33. Sun, Xiaolei & Chen, Xiuwen & Wang, Jun & Li, Jianping, 2020. "Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    34. Nicola, Francesca de & De Pace, Pierangelo & Hernandez, Manuel A., 2016. "Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment," Energy Economics, Elsevier, vol. 57(C), pages 28-41.
    35. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
    36. Lucotte, Yannick, 2016. "Co-movements between crude oil and food prices: A post-commodity boom perspective," Economics Letters, Elsevier, vol. 147(C), pages 142-147.
    37. Aurélien Leroy & Yannick Lucotte, 2016. "Co-movements between crude oil and food prices: A post-commodity boom perspective," Post-Print hal-03528419, HAL.
    38. Main, Scott & Irwin, Scott H. & Sanders, Dwight R. & Smith, Aaron, 2018. "Financialization and the returns to commodity investments," Journal of Commodity Markets, Elsevier, vol. 10(C), pages 22-28.
    39. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    40. Ji, Qiang & Bahloul, Walid & Geng, Jiang-Bo & Gupta, Rangan, 2020. "Trading behaviour connectedness across commodity markets: Evidence from the hedgers’ sentiment perspective," Research in International Business and Finance, Elsevier, vol. 52(C).
    41. Shiferaw, Yegnanew A., 2019. "Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    42. Wang, Yudong & Guo, Zhuangyue, 2018. "The dynamic spillover between carbon and energy markets: New evidence," Energy, Elsevier, vol. 149(C), pages 24-33.
    43. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
    44. Wu, Fei & Zhao, Wan-Li & Ji, Qiang & Zhang, Dayong, 2020. "Dependency, centrality and dynamic networks for international commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 118-132.
    45. Ji, Qiang & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2019. "Information interdependence among energy, cryptocurrency and major commodity markets," Energy Economics, Elsevier, vol. 81(C), pages 1042-1055.
    46. Ji, Qiang & Geng, Jiang-Bo & Tiwari, Aviral Kumar, 2018. "Information spillovers and connectedness networks in the oil and gas markets," Energy Economics, Elsevier, vol. 75(C), pages 71-84.
    47. Song, Yingjie & Ji, Qiang & Du, Ya-Juan & Geng, Jiang-Bo, 2019. "The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets," Energy Economics, Elsevier, vol. 84(C).
    48. Pal, Debdatta & Mitra, Subrata K., 2019. "Correlation dynamics of crude oil with agricultural commodities: A comparison between energy and food crops," Economic Modelling, Elsevier, vol. 82(C), pages 453-466.
    49. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2017. "The relationship between regional natural gas markets and crude oil markets from a multi-scale nonlinear Granger causality perspective," Energy Economics, Elsevier, vol. 67(C), pages 98-110.
    50. Luo, Jiawen & Ji, Qiang, 2018. "High-frequency volatility connectedness between the US crude oil market and China's agricultural commodity markets," Energy Economics, Elsevier, vol. 76(C), pages 424-438.
    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. Dai, Xingyu & Dai, Peng-Fei & Wang, Qunwei & Ouyang, Zhi-Yi, 2023. "The impact of energy-exporting countries’ EPUs on China’s energy futures investors: Risk preference, investment position and investment horizon," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
    3. Bouri, Elie & Lei, Xiaojie & Xu, Yahua & Zhang, Hongwei, 2023. "Connectedness in implied higher-order moments of precious metals and energy markets," Energy, Elsevier, vol. 263(PB).
    4. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Wang, Xiaoxuan & Gao, Xiangyun & Wu, Tao & Sun, Xiaotian, 2022. "Dynamic multiscale analysis of causality among mining stock prices," Resources Policy, Elsevier, vol. 77(C).
    6. Lin, Guancen & Lin, Aijing & Mi, Yujia & Gu, Danlei, 2023. "Measurement of information transfer based on phase increment transfer entropy," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    7. Cheng, Zishu & Li, Mingchen & Cui, Ruhong & Wei, Yunjie & Wang, Shouyang & Hong, Yongmiao, 2024. "The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    8. Agyei, Samuel Kwaku & Umar, Zaghum & Bossman, Ahmed & Teplova, Tamara, 2023. "Dynamic connectedness between global commodity sectors, news sentiment, and sub-Saharan African equities," Emerging Markets Review, Elsevier, vol. 56(C).
    9. Dhifaoui, Zouhaier & Khalfaoui, Rabeh & Abedin, Mohammad Zoynul & Shi, Baofeng, 2022. "Quantifying information transfer among clean energy, carbon, oil, and precious metals: A novel transfer entropy-based approach," Finance Research Letters, Elsevier, vol. 49(C).
    10. Liu, Yanqiong & Guo, Yaoqi & Wei, Qing, 2024. "Time-varying and multi-scale analysis of copper price influencing factors based on LASSO and EMD methods," Journal of Commodity Markets, Elsevier, vol. 34(C).
    11. Xu Zhang & Xian Yang & Jianping Li & Jun Hao, 2023. "Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 705-733, June.

    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. Cao, Yan & Cheng, Sheng, 2021. "Impact of COVID-19 outbreak on multi-scale asymmetric spillovers between food and oil prices," Resources Policy, Elsevier, vol. 74(C).
    2. Sun, Xiaolei & Liu, Chang & Wang, Jun & Li, Jianping, 2020. "Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    3. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
    4. Albulescu, Claudiu Tiberiu & Tiwari, Aviral Kumar & Ji, Qiang, 2020. "Copula-based local dependence among energy, agriculture and metal commodities markets," Energy, Elsevier, vol. 202(C).
    5. Claudiu Albulescu & Aviral Tiwari & Qiang Ji, 2020. "Copula-based local dependence between energy, agriculture and metal commodity markets," Papers 2003.04007, arXiv.org.
    6. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    7. Caporin, Massimiliano & Naeem, Muhammad Abubakr & Arif, Muhammad & Hasan, Mudassar & Vo, Xuan Vinh & Hussain Shahzad, Syed Jawad, 2021. "Asymmetric and time-frequency spillovers among commodities using high-frequency data," Resources Policy, Elsevier, vol. 70(C).
    8. Meng, Juan & Nie, He & Mo, Bin & Jiang, Yonghong, 2020. "Risk spillover effects from global crude oil market to China’s commodity sectors," Energy, Elsevier, vol. 202(C).
    9. Naeem, Muhammad Abubakr & Hasan, Mudassar & Arif, Muhammad & Suleman, Muhammad Tahir & Kang, Sang Hoon, 2022. "Oil and gold as a hedge and safe-haven for metals and agricultural commodities with portfolio implications," Energy Economics, Elsevier, vol. 105(C).
    10. Kang, Sang Hoon & Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu & Yoon, Seong-Min, 2019. "Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1," Energy Economics, Elsevier, vol. 84(C).
    11. Hanif, Waqas & Areola Hernandez, Jose & Shahzad, Syed Jawad Hussain & Yoon, Seong-Min, 2021. "Tail dependence risk and spillovers between oil and food prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 195-209.
    12. Karel Janda & Ladislav Kristoufek, 2019. "The relationship between fuel and food prices: Methods, outcomes, and lessons for commodity price risk management," CAMA Working Papers 2019-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Hu, Min & Zhang, Dayong & Ji, Qiang & Wei, Lijian, 2020. "Macro factors and the realized volatility of commodities: A dynamic network analysis," Resources Policy, Elsevier, vol. 68(C).
    14. Jiang, Yonghong & Jiang, Cheng & Nie, He & Mo, Bin, 2019. "The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses," Energy, Elsevier, vol. 166(C), pages 577-586.
    15. Wu, Fei & Zhao, Wan-Li & Ji, Qiang & Zhang, Dayong, 2020. "Dependency, centrality and dynamic networks for international commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 118-132.
    16. Xu Zhang & Xian Yang & Jianping Li & Jun Hao, 2023. "Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 705-733, June.
    17. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
    18. Cuilin Li & Ya-Juan Du & Qiang Ji & Jiang-bo Geng, 2019. "Multiscale Market Integration and Nonlinear Granger Causality between Natural Gas Futures and Physical Markets," Sustainability, MDPI, vol. 11(19), pages 1-23, October.
    19. Chen, Peng & He, Limin & Yang, Xuan, 2021. "On interdependence structure of China's commodity market," Resources Policy, Elsevier, vol. 74(C).
    20. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(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:riibaf:v:55:y:2021:i:c:s0275531920302002. 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/ribaf .

    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.