IDEAS home Printed from https://ideas.repec.org/a/spr/snbeco/v3y2023i10d10.1007_s43546-023-00572-8.html
   My bibliography  Save this article

Examining the asymmetric information flow between pairs of gold, silver, and oil: a transfer entropy approach

Author

Listed:
  • Parthajit Kayal

    (Madras School of Economics)

  • Moinak Maiti

    (Independent Researcher)

Abstract

The present study examines the asymmetric information flow between bivariate pairs of gold, silver, and oil daily returns for the period: 1 September 2000 to 5 May 2022 with a special emphasis on crisis periods. We use Shannon and Rényi entropy transfer techniques instead of the commonly used Granger causality approach to get robust estimates while allowing for nonlinear, non-parametric, and asymmetric relationships in bivariate returns series. The study’s findings support a unidirectional information flow from silver to gold, but not the other way around. Similarly, over the whole sample, a unidirectional information flow from oil to gold is observed but during the financial crisis of 2008–09, the flow of information is reversed from gold to oil. Furthermore, during COVID, there is no statistically significant information transfer between “gold and oil.” Throughout the study period, a bidirectional information flow between “silver and oil” is observed. During the financial crisis, however, there is no statistically significant information flow between “silver and oil.” As a result, portfolios based on “gold and oil” or “silver and oil” would be risky. Policymakers and investors should recognize dynamic interrelationships across market conditions and leverage the value of time-sensitive information flow for risk assessment and portfolios.

Suggested Citation

  • Parthajit Kayal & Moinak Maiti, 2023. "Examining the asymmetric information flow between pairs of gold, silver, and oil: a transfer entropy approach," SN Business & Economics, Springer, vol. 3(10), pages 1-22, October.
  • Handle: RePEc:spr:snbeco:v:3:y:2023:i:10:d:10.1007_s43546-023-00572-8
    DOI: 10.1007/s43546-023-00572-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43546-023-00572-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43546-023-00572-8?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. Benedetto, Francesco & Mastroeni, Loretta & Quaresima, Greta & Vellucci, Pierluigi, 2020. "Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis," Energy Economics, Elsevier, vol. 89(C).
    2. Kim, Jong-Min & Lee, Namgil & Hwang, Sun Young, 2020. "A Copula Nonlinear Granger Causality," Economic Modelling, Elsevier, vol. 88(C), pages 420-430.
    3. Li, Jianping & Li, Jingyu & Zhu, Xiaoqian & Yao, Yinhong & Casu, Barbara, 2020. "Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S," International Review of Financial Analysis, Elsevier, vol. 71(C).
    4. Allaudeen Hameed & Randall Morck & Jianfeng Shen & Bernard Yeung, 2015. "Information, Analysts, and Stock Return Comovement," The Review of Financial Studies, Society for Financial Studies, vol. 28(11), pages 3153-3187.
    5. Wang, Kuan-Min & Lee, Yuan-Ming & Thi, Thanh-Binh Nguyen, 2011. "Time and place where gold acts as an inflation hedge: An application of long-run and short-run threshold model," Economic Modelling, Elsevier, vol. 28(3), pages 806-819, May.
    6. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    7. Laopodis, Nikiforos T., 2009. "Fiscal policy and stock market efficiency: Evidence for the United States," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 633-650, May.
    8. Dimpfl, Thomas & Peter, Franziska J., 2018. "Analyzing volatility transmission using group transfer entropy," Energy Economics, Elsevier, vol. 75(C), pages 368-376.
    9. Mahdavi, Saeid & Zhou, Su, 1997. "Gold and commodity prices as leading indicators of inflation: Tests of long-run relationship and predictive performance," Journal of Economics and Business, Elsevier, vol. 49(5), pages 475-489.
    10. Diks Cees & Panchenko Valentyn, 2005. "A Note on the Hiemstra-Jones Test for Granger Non-causality," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-9, June.
    11. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    12. Moinak Maiti & Darko Vukovic & Yaroslav Vyklyuk & Zoran Grubisic, 2022. "BRICS Capital Markets Co-Movement Analysis and Forecasting," Risks, MDPI, vol. 10(5), pages 1-13, April.
    13. Jang, Hyuna & Kim, Jong-Min & Noh, Hohsuk, 2022. "Vine copula Granger causality in mean," Economic Modelling, Elsevier, vol. 109(C).
    14. Antonakakis, Nikolaos, 2012. "Exchange return co-movements and volatility spillovers before and after the introduction of euro," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1091-1109.
    15. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    16. Kočenda, Evžen & Moravcová, Michala, 2019. "Exchange rate comovements, hedging and volatility spillovers on new EU forex markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 42-64.
    17. Sari, Ramazan & Hammoudeh, Shawkat & Soytas, Ugur, 2010. "Dynamics of oil price, precious metal prices, and exchange rate," Energy Economics, Elsevier, vol. 32(2), pages 351-362, March.
    18. Dimpfl Thomas & Peter Franziska Julia, 2013. "Using transfer entropy to measure information flows between financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 85-102, February.
    19. Toan Luu Duc Huynh, 2019. "Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas," JRFM, MDPI, vol. 12(2), pages 1-19, April.
    20. Jain, Anshul & Ghosh, Sajal, 2013. "Dynamics of global oil prices, exchange rate and precious metal prices in India," Resources Policy, Elsevier, vol. 38(1), pages 88-93.
    21. Petr Jizba & Hagen Kleinert & Mohammad Shefaat, 2011. "Renyi's information transfer between financial time series," Papers 1106.5913, arXiv.org, revised Jan 2012.
    22. Soytas, Ugur & Sari, Ramazan & Hammoudeh, Shawkat & Hacihasanoglu, Erk, 2009. "World oil prices, precious metal prices and macroeconomy in Turkey," Energy Policy, Elsevier, vol. 37(12), pages 5557-5566, December.
    23. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
    24. Dimpfl, Thomas & Peter, Franziska J., 2014. "The impact of the financial crisis on transatlantic information flows: An intraday analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 1-13.
    25. Bampinas Georgios & Panagiotidis Theodore, 2015. "On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 657-668, December.
    26. Ferreira, Paulo & Dionísio, Andreia & Movahed, S.M.S., 2017. "Assessment of 48 Stock markets using adaptive multifractal approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 730-750.
    27. Das, Debojyoti & Bhowmik, Puja & Jana, R.K., 2018. "A multiscale analysis of stock return co-movements and spillovers: Evidence from Pacific developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 379-393.
    28. Martin Hoesli & Kustrim Reka, 2013. "Volatility Spillovers, Comovements and Contagion in Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 1-35, July.
    29. Jizba, Petr & Kleinert, Hagen & Shefaat, Mohammad, 2012. "Rényi’s information transfer between financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(10), pages 2971-2989.
    30. Tihana Škrinjarić & Derick Quintino & Paulo Ferreira, 2021. "Transfer Entropy Approach for Portfolio Optimization: An Empirical Approach for CESEE Markets," JRFM, MDPI, vol. 14(8), pages 1-12, August.
    31. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    32. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
    33. Prince Mensah Osei & Anokye M. Adam, 2020. "Quantifying the Information Flow between Ghana Stock Market Index and Its Constituents Using Transfer Entropy," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, August.
    34. Moinak Maiti & Parthajit Kayal, 2022. "Asymmetric Information Flow between Exchange Rate, Oil, and Gold: New Evidence from Transfer Entropy Approach," JRFM, MDPI, vol. 16(1), pages 1-14, December.
    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. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    2. Awaworyi Churchill, Sefa & Inekwe, John & Ivanovski, Kris & Smyth, Russell, 2019. "Dynamics of oil price, precious metal prices and the exchange rate in the long-run," Energy Economics, Elsevier, vol. 84(C).
    3. Neto, David, 2022. "Revisiting spillovers between investor attention and cryptocurrency markets using noisy independent component analysis and transfer entropy," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    4. Dutta, Anupam & Bouri, Elie & Roubaud, David, 2019. "Nonlinear relationships amongst the implied volatilities of crude oil and precious metals," Resources Policy, Elsevier, vol. 61(C), pages 473-478.
    5. Kanjilal, Kakali & Ghosh, Sajal, 2017. "Dynamics of crude oil and gold price post 2008 global financial crisis – New evidence from threshold vector error-correction model," Resources Policy, Elsevier, vol. 52(C), pages 358-365.
    6. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    7. Nie, Chun-Xiao, 2023. "Time-varying characteristics of information flow networks in the Chinese market: An analysis based on sector indices," Finance Research Letters, Elsevier, vol. 54(C).
    8. Shahbaz, Muhammad & Balcilar, Mehmet & Abidin Ozdemir, Zeynel, 2017. "Does oil predict gold? A nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 52(C), pages 257-265.
    9. Semei Coronado & Rebeca Jim'enez-Rodr'iguez & Omar Rojas, 2015. "An empirical analysis of the relationships between crude oil, gold and stock markets," Papers 1510.07599, arXiv.org, revised May 2016.
    10. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    11. Mishra, Aswini Kumar & Ghate, Kshitish & Renganathan, Jayashree & Kennet, Joushita J. & Rajderkar, Nilay Pradeep, 2022. "Rolling, recursive evolving and asymmetric causality between crude oil and gold prices: Evidence from an emerging market," Resources Policy, Elsevier, vol. 75(C).
    12. Ferreira, Paulo & Almeida, Dora & Dionísio, Andreia & Bouri, Elie & Quintino, Derick, 2022. "Energy markets – Who are the influencers?," Energy, Elsevier, vol. 239(PA).
    13. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    14. Leonidas Sandoval Junior, 2014. "Dynamics in two networks based on stocks of the US stock market," Papers 1408.1728, arXiv.org, revised Aug 2014.
    15. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    16. Chen, Ning & Li, Shaofang & Lu, Shuai, 2023. "The extreme risk connectedness of the global financial system: G7 and BRICS evidence," Journal of Multinational Financial Management, Elsevier, vol. 69(C).
    17. Alomari, Mohammad & Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Extreme return spillovers and connectedness between crude oil and precious metals futures markets: Implications for portfolio management," Resources Policy, Elsevier, vol. 79(C).
    18. Huang, Jianbai & Dong, Xuesong & Chen, Jinyu & Zhong, Meirui, 2022. "Do oil prices and economic policy uncertainty matter for precious metal returns? New insights from a TVP-VAR framework," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 433-445.
    19. Xiao, Di & Wang, Jun, 2020. "Dynamic complexity and causality of crude oil and major stock markets," Energy, Elsevier, vol. 193(C).
    20. Aslanidis, Nektarios & Bariviera, Aurelio F. & López, Óscar G., 2022. "The link between cryptocurrencies and Google Trends attention," Finance Research Letters, Elsevier, vol. 47(PA).

    More about this item

    Keywords

    Transfer entropy; Rényi; Shannon; Crisis; Gold; Silver; Oil;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:spr:snbeco:v:3:y:2023:i:10:d:10.1007_s43546-023-00572-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.