IDEAS home Printed from https://ideas.repec.org/a/vrs/ecobur/v7y2021i4p28-53n2.html
   My bibliography  Save this article

Agricultural commodities: An integrated approach to assess the volatility spillover and dynamic connectedness

Author

Listed:
  • Mishra Arunendra

    (Department of Food Business Management and Entrepreneurship, National Institute of Food Technology Entrepreneurship and Management, Sonipat (Delhi NCR) – 131028, India)

  • Kumar R Prasanth

    (Department of Food Business Management and Entrepreneurship, National Institute of Food Technology Entrepreneurship and Management, Sonipat (Delhi NCR) – 131028, India)

Abstract

In this article the dynamic connectedness between the five agricultural commodities is examined by implementing the Diebold and Yılmaz (VAR based) and Time--Varying Parameter Vector Autoregressions (TVP-VAR) measures for understanding the time-varying variance-covariance mechanism using daily data for the period of 2005 to 2019. The findings reveal that at an overall level all the commodity prices are less susceptible to significant volatility shocks from other commodities specifically before the introduction of the pan-India electronic trading portal (eNAM). Cotton prices do not show any variation due to spillover from others for the entire study period. The volatility spillover is visible post eNAM period particularly for the commodity stock prices. Whereas at an overall level the total directional connectedness has gone down in the post eNAM era. The network analysis suggests that the commodity stock prices show a stronger association as compared to market prices. Generally commodity prices show volatility connectedness but with respect to their own market which means strong spillover is missing among both the markets.

Suggested Citation

  • Mishra Arunendra & Kumar R Prasanth, 2021. "Agricultural commodities: An integrated approach to assess the volatility spillover and dynamic connectedness," Economics and Business Review, Sciendo, vol. 7(4), pages 28-53, December.
    • Handle: RePEc:vrs:ecobur:v:7:y:2021:i:4:p:28-53:n:2
    DOI: 10.18559/ebr.2021.4.3
    as

    Download full text from publisher

    File URL: https://doi.org/10.18559/ebr.2021.4.3
    Download Restriction: no
    File URL: https://libkey.io/10.18559/ebr.2021.4.3?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
    ---><---

    References listed on IDEAS

    as
    1. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    4. Sim, Nicholas & Zhou, Hongtao, 2015. "Oil prices, US stock return, and the dependence between their quantiles," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 1-8.
    5. Dahl, Roy Endré & Jonsson, Erlendur, 2018. "Volatility spillover in seafood markets," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 44-59.
    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. Ying-Ying Shen & Zhi-Qiang Jiang & Jun-Chao Ma & Gang-Jin Wang & Wei-Xing Zhou, 2022. "Sector connectedness in the Chinese stock markets," Empirical Economics, Springer, vol. 62(2), pages 825-852, February.
    2. Wang, Gang-Jin & Xiong, Lu & Zhu, You & Xie, Chi & Foglia, Matteo, 2022. "Multilayer network analysis of investor sentiment and stock returns," Research in International Business and Finance, Elsevier, vol. 62(C).
    3. Mensi, Walid & Vo, Xuan Vinh & Ko, Hee-Un & Kang, Sang Hoon, 2023. "Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 558-580.
    4. Feng, Yusen & Wang, Gang-Jin & Zhu, You & Xie, Chi, 2023. "Systemic risk spillovers and the determinants in the stock markets of the Belt and Road countries," Emerging Markets Review, Elsevier, vol. 55(C).
    5. Elie Bouri & David Gabauer & Rangan Gupta & Harald Kinateder, 2023. "Geopolitical Risk and Inflation Spillovers across European and North American Economies," Working Papers 202304, University of Pretoria, Department of Economics.
    6. Liu, Zhenhua & Shi, Xunpeng & Zhai, Pengxiang & Wu, Shan & Ding, Zhihua & Zhou, Yuqin, 2021. "Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach," Resources Policy, Elsevier, vol. 74(C).
    7. Urom, Christian & Abid, Ilyes & Guesmi, Khaled & Chevallier, Julien, 2020. "Quantile spillovers and dependence between Bitcoin, equities and strategic commodities," Economic Modelling, Elsevier, vol. 93(C), pages 230-258.
    8. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    9. Juncal Cunado & David Gabauer & Rangan Gupta, 2024. "Realized volatility spillovers between energy and metal markets: a time-varying connectedness approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-17, December.
    10. Aysan, Ahmet Faruk & Batten, Jonathan & Gozgor, Giray & Khalfaoui, Rabeh & Nanaeva, Zhamal, 2024. "Metaverse and financial markets: A quantile-time-frequency connectedness analysis," Research in International Business and Finance, Elsevier, vol. 72(PB).
    11. Christian Urom & Gideon Ndubuisi & Jude Ozor, 2021. "Economic activity, and financial and commodity markets’ shocks: An analysis of implied volatility indexes," International Economics, CEPII research center, issue 165, pages 51-66.
    12. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    13. Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    14. Elsayed, Ahmed H. & Asutay, Mehmet & ElAlaoui, Abdelkader O. & Bin Jusoh, Hashim, 2024. "Volatility spillover across spot and futures markets: Evidence from dual financial system," Research in International Business and Finance, Elsevier, vol. 71(C).
    15. Gabauer, David & Chatziantoniou, Ioannis & Stenfors, Alexis, 2023. "Model-free connectedness measures," Finance Research Letters, Elsevier, vol. 54(C).
    16. Tsai, I-Chun & Chiang, Shu-Hen, 2019. "Exuberance and spillovers in housing markets: Evidence from first- and second-tier cities in China," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 75-86.
    17. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
    18. Bouri, Elie & Gabauer, David & Gupta, Rangan & Tiwari, Aviral Kumar, 2021. "Volatility connectedness of major cryptocurrencies: The role of investor happiness," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    19. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    20. Ben Cheikh, Nidhaleddine & Ben Naceur, Sami & Kanaan, Oussama & Rault, Christophe, 2021. "Investigating the asymmetric impact of oil prices on GCC stock markets," Economic Modelling, Elsevier, vol. 102(C).

    More about this item

    Keywords

    dynamic connectedness; TVP-VAR; price volatility; volatility spillover; agricultural commodities; network diagrams;
    All these keywords.

    JEL classification:

    • 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
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:vrs:ecobur:v:7:y:2021:i:4:p:28-53:n:2. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.