IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v79y2022ics0301420722004639.html
   My bibliography  Save this article

Booms in commodities price: Assessing disorder and similarity over economic cycles

Author

Listed:
  • Fernandes, Leonardo H.S.
  • de Araujo, Fernando H.A.
  • Silva, José W.L.
  • Tabak, Benjamin Miranda

Abstract

This paper provides an overview of the commodities market, considering three relevant attributes: predictability, similarity, and efficiency. We examine the monthly spot and futures prices time series for 22 commodities from January 1984 until January 2022 with 457 observations. We estimate the permutation entropy (Es) and Fisher information measure Fs using the Bandt & Pompe method (BPM). We employ the value of these two complexity measures to construct the Shannon–Fisher Causality Plane (SFCP), which allows us to evaluate the disorder and assess the randomness present in the monthly spot and futures prices time series for these commodities. Moreover, we apply Es and Fs to classify the commodities using complexity hierarchy. We find that the commodities that are located farther from the random ideal position (Es=1,Fs=0) in the SFCP, such as Natural gas, Europe; Iron ore, cfr spot, and Potassium chloride are marked by lower entropy, higher predictability and lower efficiency. In contrast, the commodities that are located near the random ideal position (Es=1,Fs=0) in the SFCP, such as Crude oil-Brent; Crude oil-average, and Silver are characterized by higher entropy, lower predictability and higher efficiency. The K-means algorithm and the hierarchical cluster grouped commodities into only three distinct groups, which is a strong indication that commodity prices have very similar behavior.

Suggested Citation

  • Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Silva, José W.L. & Tabak, Benjamin Miranda, 2022. "Booms in commodities price: Assessing disorder and similarity over economic cycles," Resources Policy, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:jrpoli:v:79:y:2022:i:c:s0301420722004639
    DOI: 10.1016/j.resourpol.2022.103020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420722004639
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2022.103020?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. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2021. "Insights from the (in)efficiency of Chinese sectoral indices during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    2. Sensoy, Ahmet, 2019. "The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies," Finance Research Letters, Elsevier, vol. 28(C), pages 68-73.
    3. Aslam, Faheem & Zil-e-huma, & Bibi, Rashida & Ferreira, Paulo, 2022. "Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis," Resources Policy, Elsevier, vol. 75(C).
    4. Cajueiro, Daniel O. & Tabak, Benjamin M., 2007. "Time-varying long-range dependence in US interest rates," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 360-367.
    5. Umar, Zaghum & Gubareva, Mariya & Teplova, Tamara, 2021. "The impact of Covid-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels," Resources Policy, Elsevier, vol. 73(C).
    6. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    7. Sensoy, Ahmet & Tabak, Benjamin M., 2016. "Dynamic efficiency of stock markets and exchange rates," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 353-371.
    8. Akhtaruzzaman, Md & Boubaker, Sabri & Lucey, Brian M. & Sensoy, Ahmet, 2021. "Is gold a hedge or a safe-haven asset in the COVID–19 crisis?," Economic Modelling, Elsevier, vol. 102(C).
    9. Pincheira, Pablo & Hardy, Nicolás, 2021. "Forecasting aluminum prices with commodity currencies," Resources Policy, Elsevier, vol. 73(C).
    10. Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Energy commodities, precious metals and industrial metal markets: A nexus across different investment horizons and market conditions," Resources Policy, Elsevier, vol. 70(C).
    11. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Testing for time-varying long-range dependence in volatility for emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 577-588.
    12. Higor Y. D. Sigaki & Matjaz Perc & Haroldo V. Ribeiro, 2019. "Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market," Papers 1901.04967, arXiv.org.
    13. Selmi, Refk & Hammoudeh, Shawkat & Kasmaoui, Kamal & Sousa, Ricardo M. & Errami, Youssef, 2022. "The dual shocks of the COVID-19 and the oil price collapse: A spark or a setback for the circular economy?," Energy Economics, Elsevier, vol. 109(C).
    14. Tabak, Benjamin M. & Cajueiro, Daniel O., 2007. "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility," Energy Economics, Elsevier, vol. 29(1), pages 28-36, January.
    15. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Upward/downward multifractality and efficiency in metals futures markets: The impacts of financial and oil crises," Resources Policy, Elsevier, vol. 76(C).
    16. Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices," Resources Policy, Elsevier, vol. 69(C).
    17. B. M. Tabak & T. R. Serra & D. O. Cajueiro, 2010. "Topological properties of commodities networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 74(2), pages 243-249, March.
    18. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    19. Aurelio F. Bariviera & Luciano Zunino & Osvaldo A. Rosso, 2018. "An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers," Papers 1808.01926, arXiv.org.
    20. Kristoufek, Ladislav & Vosvrda, Miloslav, 2019. "Cryptocurrencies market efficiency ranking: Not so straightforward," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    21. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    22. Bouoiyour, Jamal & Selmi, Refk & Hammoudeh, Shawkat & Wohar, Mark E., 2019. "What are the categories of geopolitical risks that could drive oil prices higher? Acts or threats?," Energy Economics, Elsevier, vol. 84(C).
    23. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets," Energy Economics, Elsevier, vol. 98(C).
    24. Niu, Hongli & Hu, Ziang, 2021. "Information transmission and entropy-based network between Chinese stock market and commodity futures market," Resources Policy, Elsevier, vol. 74(C).
    25. DIMA, Bogdan & DIMA, Ştefana Maria & IOAN, Roxana, 2021. "Remarks on the behaviour of financial market efficiency during the COVID-19 pandemic. The case of VIX," Finance Research Letters, Elsevier, vol. 43(C).
    26. Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    27. 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.
    28. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
    29. Memon, Bilal Ahmed & Yao, Hongxing & Naveed, Hafiz Muhammad, 2022. "Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets," Resources Policy, Elsevier, vol. 77(C).
    30. Ji, Qiang & Zhang, Dayong & Zhao, Yuqian, 2020. "Searching for safe-haven assets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 71(C).
    31. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2020. "Spillovers and co-movements between precious metals and energy markets: Implications on portfolio management," Resources Policy, Elsevier, vol. 69(C).
    32. Guo, Yaoqi & Yao, Shanshan & Cheng, Hui & Zhu, Wensong, 2020. "China's copper futures market efficiency analysis: Based on nonlinear Granger causality and multifractal methods," Resources Policy, Elsevier, vol. 68(C).
    33. Guo, Shanwen & Wang, Qibin & Hordofa, Tolassa Temesgen & Kaur, Prabjot & Nguyen, Ngoc Quynh & Maneengam, Apichit, 2022. "Does COVID-19 pandemic cause natural resources commodity prices volatility? Empirical evidence from China," Resources Policy, Elsevier, vol. 77(C).
    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. Hu, Guoheng & Liu, Shan & Wu, Guo & Hu, Peng & Li, Ruiqi & Chen, Liujie, 2023. "Economic policy uncertainty, geopolitical risks, and the heterogeneity of commodity price fluctuations in China ——an empirical study based on TVP-SV-VAR model," Resources Policy, Elsevier, vol. 85(PA).

    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. Fernandes, Leonardo H.S. & Bouri, Elie & Silva, José W.L. & Bejan, Lucian & de Araujo, Fernando H.A., 2022. "The resilience of cryptocurrency market efficiency to COVID-19 shock," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Fernandes, Leonardo H.S. & Silva, José W.L. & de Araujo, Fernando H.A. & Ferreira, Paulo & Aslam, Faheem & Tabak, Benjamin Miranda, 2022. "Interplay multifractal dynamics among metal commodities and US-EPU," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    3. Chen, Lin & Min, Feng & Liu, Wenhua & Wen, Fenghua, 2022. "The Impact of the Infectious diseases and Commodity on Stock Markets," Finance Research Letters, Elsevier, vol. 47(PB).
    4. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    5. Rehman, Mobeen Ur & Owusu Junior, Peterson & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Time-varying risk analysis for commodity futures," Resources Policy, Elsevier, vol. 78(C).
    6. Liu, Min & Lee, Chien-Chiang, 2022. "Is gold a long-run hedge, diversifier, or safe haven for oil? Empirical evidence based on DCC-MIDAS," Resources Policy, Elsevier, vol. 76(C).
    7. Dai, Xingyu & Li, Matthew C. & Xiao, Ling & Wang, Qunwei, 2022. "COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis," Resources Policy, Elsevier, vol. 79(C).
    8. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    9. Kamal, Javed Bin & Wohar, Mark & Kamal, Khaled Bin, 2022. "Do gold, oil, equities, and currencies hedge economic policy uncertainty and geopolitical risks during covid crisis?," Resources Policy, Elsevier, vol. 78(C).
    10. Soni, Rajat Kumar & Nandan, Tanuj, 2022. "Modeling Covid-19 contagious effect between asset markets and commodity futures in India," Resources Policy, Elsevier, vol. 79(C).
    11. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2011. "Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 817-827.
    12. Roy, Archi & Soni, Anchal & Deb, Soudeep, 2023. "A wavelet-based methodology to compare the impact of pandemic versus Russia–Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets," Energy Economics, Elsevier, vol. 124(C).
    13. Bossman, Ahmed & Agyei, Samuel Kwaku, 2022. "Interdependence structure of global commodity classes and African equity markets: A vector wavelet coherence analysis," Resources Policy, Elsevier, vol. 79(C).
    14. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2010. "Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5759-5768.
    15. Abdullah, Mohammad & Chowdhury, Mohammad Ashraful Ferdous & Sulong, Zunaidah, 2023. "Asymmetric efficiency and connectedness among green stocks, halal tourism stocks, cryptocurrencies, and commodities: Portfolio hedging implications," Resources Policy, Elsevier, vol. 81(C).
    16. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Espinosa-Paredes, Gilberto, 2012. "Is the US stock market becoming weakly efficient over time? Evidence from 80-year-long data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5643-5647.
    17. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    18. Ghazani, Majid Mirzaee & Khosravi, Reza & Caporin, Massimiliano, 2023. "Analyzing interconnection among selected commodities in the 2008 global financial crisis and the COVID-19 pandemic," Resources Policy, Elsevier, vol. 80(C).
    19. Qian Wang & Yu Wei & Yifeng Zhang & Yuntong Liu, 2023. "Evaluating the Safe-Haven Abilities of Bitcoin and Gold for Crude Oil Market: Evidence During the COVID-19 Pandemic," Evaluation Review, , vol. 47(3), pages 391-432, June.
    20. Ruan, Qingsong & Yang, Bingchan & Ma, Guofeng, 2017. "Detrended cross-correlation analysis on RMB exchange rate and Hang Seng China Enterprises Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 91-108.

    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:79:y:2022:i:c:s0301420722004639. 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.