IDEAS home Printed from https://ideas.repec.org/p/anc/wpaper/471.html
   My bibliography  Save this paper

Who Moves First? Commodity Price Interdependence Through Time-Varying Granger Causality

Author

Listed:
  • Roberto Esposti

    (Department of Economics and Social Sciences, Marche Polytechnic University)

Abstract

This paper investigates the interdependence among commodity prices. Commodities belonging to three different groups (energy commodities, metals, agricultural commodities) are considered. The analysis is performed via a battery of time-varying Granger causality tests. They allow assessing whether price interdependence occurs and to identify the candidate first movers. These tests also allow observing how long and in which sub-periods these causality relationships occur. The approach is applied to the monthly prices of eleven commodities over the 1980-2021 period. Results suggest that interdependence is weak for energy and agricultural commodities and often concerns limited time periods, while it seems stronger and longer lasting among metals. Moreover, if an overall price driver has to be identified, agricultural commodities more than oil seem to be the best candidates.

Suggested Citation

  • Roberto Esposti, 2022. "Who Moves First? Commodity Price Interdependence Through Time-Varying Granger Causality," Working Papers 471, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wpaper:471
    as

    Download full text from publisher

    File URL: http://docs.dises.univpm.it/web/quaderni/pdf/471.pdf
    File Function: First version, 2022
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ding, Shusheng & Cui, Tianxiang & Zheng, Dandan & Du, Min, 2021. "The effects of commodity financialization on commodity market volatility," Resources Policy, Elsevier, vol. 73(C).
    2. Yamada, Hiroshi & Toda, Hiro Y., 1998. "Inference in possibly integrated vector autoregressive models: some finite sample evidence," Journal of Econometrics, Elsevier, vol. 86(1), pages 55-95, June.
    3. Roberto Esposti & Giulia Listorti, 2013. "Agricultural price transmission across space and commodities during price bubbles," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 125-139, January.
    4. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    5. Corradi, Valentina & Swanson, Norman R., 2006. "The effect of data transformation on common cycle, cointegration, and unit root tests: Monte Carlo results and a simple test," Journal of Econometrics, Elsevier, vol. 132(1), pages 195-229, May.
    6. Joseph P Byrne & Ryuta Sakemoto & Bing Xu, 2020. "Commodity price co-movement: heterogeneity and the time-varying impact of fundamentals [Oil price shocks and the stock market: evidence from Japan]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(2), pages 499-528.
    7. Listorti, Giulia & Esposti, Roberto, 2012. "Horizontal Price Transmission in Agricultural Markets: Fundamental Concepts and Open Empirical Issues," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-28, April.
    8. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    9. Zhao, Zhao & Wen, Huwei & Li, Ke, 2021. "Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China," Economic Modelling, Elsevier, vol. 94(C), pages 780-788.
    10. Dabin Wang & William G. Tomek, 2007. "Commodity Prices and Unit Root Tests," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(4), pages 873-889.
    11. Esposti, Roberto, 2024. "Dating common commodity price and inflation shocks with alternative approaches," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 13(2), July.
    12. Jesús Otero & Christopher F Baum, 2021. "Unit-root tests for explosive behavior," Stata Journal, StataCorp LP, vol. 21(4), pages 999-1020, December.
    13. Leybourne, S J, 1995. "Testing for Unit Roots Using Forward and Reverse Dickey-Fuller Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 57(4), pages 559-571, November.
    14. Shuping Shi & Stan Hurn & Peter C B Phillips, 2020. "Causal Change Detection in Possibly Integrated Systems: Revisiting the Money–Income Relationship [Energy Consumption and Economic Growth in the United States]," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 18(1), pages 158-180.
    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. Esposti, Roberto, 2024. "Dating common commodity price and inflation shocks with alternative approaches," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 13(2), July.
    2. İbrahim Özmen & Şerife Özşahin, 2023. "Effects of global energy and price fluctuations on Turkey's inflation: new evidence," Economic Change and Restructuring, Springer, vol. 56(4), pages 2695-2728, August.
    3. David Gabauer & Rangan Gupta & Jacobus Nel & Woraphon Yamaka, 2021. "Time-Varying Predictability of Labor Productivity on Inequality in United Kingdom," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(3), pages 771-788, June.
    4. Ren, Xiaohang & Fu, Chenjia & Jin, Chenglu & Li, Yuyi, 2024. "Dynamic causality between global supply chain pressures and China's resource industries: A time-varying Granger analysis," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    5. Ren, Xiaohang & Li, Jingyao & He, Feng & Lucey, Brian, 2023. "Impact of climate policy uncertainty on traditional energy and green markets: Evidence from time-varying granger tests," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    6. Afonso, António & Mignon, Valérie & Saadaoui, Jamel, 2024. "On the time-varying impact of China's bilateral political relations on its trading partners: “Doux commerce” or “trade follows the flag”?," China Economic Review, Elsevier, vol. 85(C).
    7. Esposti, Roberto, 2021. "On the long-term common movement of resource and commodity prices.A methodological proposal," Resources Policy, Elsevier, vol. 72(C).
    8. Liu, Guangqiang & Zeng, Qing & Lei, Juan, 2022. "Dynamic risks from climate policy uncertainty: A case study for the natural gas market," Resources Policy, Elsevier, vol. 79(C).
    9. Hong, Yanran & Cao, Shijiao & Xu, Pengfei & Pan, Zhigang, 2024. "Interpreting the effect of global economic risks on crude oil market: A supply-demand perspective," International Review of Financial Analysis, Elsevier, vol. 91(C).
    10. Aharon, David Y. & Azman Aziz, Mukhriz Izraf & Kallir, Ido, 2023. "Oil price shocks and inflation: A cross-national examination in the ASEAN5+3 countries," Resources Policy, Elsevier, vol. 82(C).
    11. Adeosun, Opeoluwa Adeniyi & Tabash, Mosab I. & Anagreh, Suhaib, 2022. "Oil price and economic performance: Additional evidence from advanced economies," Resources Policy, Elsevier, vol. 77(C).
    12. Rosa, Franco & Vasciaveo, Michela & Weaver, Robert D., 2014. "Agricultural and oil commodities: price transmission and market integration between US and Italy," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 3(2), pages 1-25, August.
    13. Lorenzo Trapani, 2021. "Testing for strict stationarity in a random coefficient autoregressive model," Econometric Reviews, Taylor & Francis Journals, vol. 40(3), pages 220-256, April.
    14. Joseph, Kishore & Garcia, Philip & Peterson, Paul E., 2016. "Does the Boxed Beef Price Inform the Live Cattle Futures Price?," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236166, Agricultural and Applied Economics Association.
    15. Hicham Ayad & Ousama Ben-Salha & Miloud Ouafi, 2023. "Do oil prices predict the exchange rate in Algeria? Time, frequency, and time‐varying Granger causality analysis," Economic Change and Restructuring, Springer, vol. 56(5), pages 3545-3566, October.
    16. Valérie Mignon & Christophe Hurlin, 2005. "Une synthèse des tests de racine unitaire sur données de panel," Économie et Prévision, Programme National Persée, vol. 169(3), pages 253-294.
    17. Polat, Onur & Ozcan, Burcu & Ertuğrul, Hasan Murat & Atılgan, Emre & Özün, Alper, 2024. "Fintech: A Conduit for sustainability and renewable energy? Evidence from R2 connectedness analysis," Resources Policy, Elsevier, vol. 94(C).
    18. Ilyasov, Jarilkasin & Götz, Linde & Akramov, Kamiljon T. & Dorosh, Paul A. & Glauben, Thomas, 2016. "Market integration and price transmission in Tajikistan’s wheat markets: Rising like rockets but falling like feathers?:," IFPRI discussion papers 1547, International Food Policy Research Institute (IFPRI).
    19. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    20. Seyi Saint Akadiri & Andrew Adewale Alola & Ahdi Noomen Ajmi, 2022. "Trilemma of pandemic-related health emergency, economic policy uncertainty and partisan conflict in the United States: A time-varying analysis evidence," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(4), pages 771-784, October.

    More about this item

    Keywords

    Commodity Prices; Time Varying Granger Causality; Price Interdependence.;
    All these keywords.

    JEL classification:

    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:anc:wpaper:471. 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: Maurizio Mariotti (email available below). General contact details of provider: https://edirc.repec.org/data/deancit.html .

    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.