IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i2p58-d1038318.html
   My bibliography  Save this article

Is There Any Pattern Regarding the Vulnerability of Smart Contracts in the Food Supply Chain to a Stressed Event? A Quantile Connectedness Investigation

Author

Listed:
  • Bikramaditya Ghosh

    (Symbiosis Institute of Business Management, Symbiosis International, Deemed University, Bengaluru 560100, India
    FLAM Department, Harper Adams University, Newport TF10 8NB, UK)

  • Dimitrios Paparas

    (FLAM Department, Harper Adams University, Newport TF10 8NB, UK)

Abstract

Blockchain can support the food supply chain in several aspects. Particularly, food traceability and trading across pre-existing contracts can make the supply chain fast, error-free, and support in detecting potential fraud. A proper algorithm, keeping in mind specific geographic, demographic, and additional essential parameters, would let the automated market maker (AMM) supply ample liquidity to pre-determined orders. AMMs are usually run by a set of sequential algorithms called a ‘smart contract’ (SM). Appropriate use of SM reduces food waste, contamination, extra or no delivery in due course, and, possibly most significantly, increases traceability. However, SM has definite vulnerabilities, making it less adaptable at times. We are investigating whether they are genuinely vulnerable during stressful periods or not. We considered seven SM platforms, namely, Fabric, Ethereum (ETH), Waves, NEM (XEM), Tezos (XTZ), Algorand (ALGO), and Stellar (XLM), as the proxies for food supply-chain-based smart contracts from 29 August 2021 to 5 October 2022. This period coincides with three stressed events: Delta (Covid II), Omicron (Covid III), and the Russian invasion of Ukraine. We found strong traces of risk transmission, comovement, and interdependence of SM return among the diversified SMs; however, the SMs focused on the food supply chain ended up as net receivers of shocks at both of the extreme tails. All these SMs share a stronger connection in both positive shocks (bullish) and negative shocks (bearish).

Suggested Citation

  • Bikramaditya Ghosh & Dimitrios Paparas, 2023. "Is There Any Pattern Regarding the Vulnerability of Smart Contracts in the Food Supply Chain to a Stressed Event? A Quantile Connectedness Investigation," JRFM, MDPI, vol. 16(2), pages 1-12, January.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:2:p:58-:d:1038318
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/2/58/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/16/2/58/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Routledge, Bryan & Zetlin-Jones, Ariel, 2022. "Currency stability using blockchain technology," Journal of Economic Dynamics and Control, Elsevier, vol. 142(C).
    3. Ludger Linnemann & Roland Winkler, 2016. "Estimating nonlinear effects of fiscal policy using quantile regression methods," Oxford Economic Papers, Oxford University Press, vol. 68(4), pages 1120-1145.
    4. Saeed, Tareq & Bouri, Elie & Alsulami, Hamed, 2021. "Extreme return connectedness and its determinants between clean/green and dirty energy investments," Energy Economics, Elsevier, vol. 96(C).
    5. Chen, Jinyu & Liang, Zhipeng & Ding, Qian & Liu, Zhenhua, 2022. "Quantile connectedness between energy, metal, and carbon markets," International Review of Financial Analysis, Elsevier, vol. 83(C).
    6. Bouri, Elie & Harb, Etienne, 2022. "The size of good and bad volatility shocks does matter for spillovers," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    7. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.
    8. Vijay Mohan, 2022. "Automated market makers and decentralized exchanges: a DeFi primer," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-48, December.
    9. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    10. Bouri, Elie & Saeed, Tareq & Vo, Xuan Vinh & Roubaud, David, 2021. "Quantile connectedness in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    11. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    12. Pham, Linh & Nguyen, Canh Phuc, 2021. "Asymmetric tail dependence between green bonds and other asset classes," Global Finance Journal, Elsevier, vol. 50(C).
    13. Olivier Blanchard & Roberto Perotti, 2002. "An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1329-1368.
    14. 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.
    15. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    16. Dianhui Mao & Fan Wang & Zhihao Hao & Haisheng Li, 2018. "Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain," IJERPH, MDPI, vol. 15(8), pages 1-21, August.
    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. Che-Pin Chen & Kai-Wen Huang & Yung-Chi Kuo, 2023. "Conditional Token: A New Model to Supply Chain Finance by Using Smart Contract in Public Blockchain," FinTech, MDPI, vol. 2(1), pages 1-35, March.

    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. Ghosh, Bikramaditya & Pham, Linh & Teplova, Tamara & Umar, Zaghum, 2023. "COVID-19 and the quantile connectedness between energy and metal markets," Energy Economics, Elsevier, vol. 117(C).
    2. Pham, Linh & Cepni, Oguzhan, 2022. "Extreme directional spillovers between investor attention and green bond markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 186-210.
    3. Zhang, Hongwei & Zhang, Yubo & Gao, Wang & Li, Yingli, 2023. "Extreme quantile spillovers and drivers among clean energy, electricity and energy metals markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. Asadi, Mehrad & Roudari, Soheil & Tiwari, Aviral Kumar & Roubaud, David, 2023. "Scrutinizing commodity markets by quantile spillovers: A case study of the Australian economy," Energy Economics, Elsevier, vol. 118(C).
    5. Zhang, Hongwei & Jin, Chen & Bouri, Elie & Gao, Wang & Xu, Yahua, 2023. "Realized higher-order moments spillovers between commodity and stock markets: Evidence from China," Journal of Commodity Markets, Elsevier, vol. 30(C).
    6. Urom, Christian & Ndubuisi, Gideon, 2023. "Do geopolitical risks and global market factors influence the dynamic dependence among regional sustainable investments and major commodities?," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 94-111.
    7. Urom, Christian & Mzoughi, Hela & Ndubuisi, Gideon & Guesmi, Khaled, 2022. "Directional predictability and time-frequency spillovers among clean energy sectors and oil price uncertainty," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 326-341.
    8. Khalfaoui, Rabeh & Mefteh-Wali, Salma & Dogan, Buhari & Ghosh, Sudeshna, 2023. "Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis," International Review of Financial Analysis, Elsevier, vol. 86(C).
    9. Dai, Zhifeng & Zhang, Xiaotong & Yin, Zhujia, 2023. "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 118(C).
    10. Evrim Mandaci, Pınar & Azimli, Asil & Mandaci, Nazif, 2023. "The impact of geopolitical risks on connectedness among natural resource commodities: A quantile vector autoregressive approach," Resources Policy, Elsevier, vol. 85(PA).
    11. Muhammad Abubakr Naeem & Sitara Karim & Aviral Kumar Tiwari, 2023. "Risk Connectedness Between Green and Conventional Assets with Portfolio Implications," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 609-637, August.
    12. Guangxi Cao & Fei Xie & Meijun Ling, 2022. "Spillover effects in Chinese carbon, energy and financial markets," International Finance, Wiley Blackwell, vol. 25(3), pages 416-434, December.
    13. Urom, C. & Ndubuisi, Gideon & Guesmi, K., 2022. "Quantile return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional asset," MERIT Working Papers 2022-017, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    14. Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Mirza, Nawazish & Umar, Muhammad, 2022. "Safe haven properties of green, Islamic, and crypto assets and investor's proclivity towards treasury and gold," Energy Economics, Elsevier, vol. 115(C).
    15. Chen, Yu & Lin, Boqiang, 2022. "Quantifying the extreme spillovers on worldwide ESG leaders' equity," International Review of Financial Analysis, Elsevier, vol. 84(C).
    16. Chen, Jinyu & Liang, Zhipeng & Ding, Qian & Liu, Zhenhua, 2022. "Extreme spillovers among fossil energy, clean energy, and metals markets: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 107(C).
    17. 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).
    18. 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.
    19. Gabauer, David & Chatziantoniou, Ioannis & Stenfors, Alexis, 2023. "Model-free connectedness measures," Finance Research Letters, Elsevier, vol. 54(C).
    20. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(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:gam:jjrfmx:v:16:y:2023:i:2:p:58-:d:1038318. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.