Skip to main content

An Agent-Oriented, Blockchain-Based Design of the Interbank Money Market Trading System

  • Conference paper
  • First Online:
Agents and Multi-Agent Systems: Technologies and Applications 2021

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 241))

Abstract

When studying the interbank money market (IMM), it is common to model banks as agents interacting through loans to tackle its complexity. However, the use of agent abstraction in the IMM is mostly limited to some specific cases. Besides, recent advancements show that it is promising to use blockchain technology to improve its security in a decentralized way. Based on this observation, this paper proposes an agent-oriented, blockchain-based design of the IMM trading systems, where the main objective is to decide on the times and methods of liquidity supply and demand by various market players based on what has been learned from the information available. The models in this paper are suitable for use by both academics and practitioners in this field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Society for Worldwide Interbank Financial Telecommunication, https://en.wikipedia.org/wiki/Society_for_Worldwide_Interbank_Financial_Telecommunication, last access on 21/02/2021.

  2. 2.

    How Blockchain Could Disrupt Banking, https://www.cbinsights.com/research/blockchain-disrupting-banking/, last access on 21/02/2021.

  3. 3.

    Interbank Market Sees Live Deployment of Blockchain Technology in Reconciliation Process, https://financialit.net/news/blockchain/interbank-market-sees-live-deployment-blockchain-technology-reconciliation-process, last access on 21/02/2021.

References

  1. Acemoglu, D., Ozdaglar, A., Tahbaz-Salehi, A.: Systemic risk and stability in financial networks. Am. Econ. Rev. 105(2), 564–608 (2015)

    Article  Google Scholar 

  2. Leventides, J., Loukaki, K., Papavassiliou, V.G.: Simulating financial contagion dynamics in random interbank networks. J. Econ. Behav. Organ. 158, 500–525 (2019)

    Article  Google Scholar 

  3. Kobayashi, T., Takaguchi, T.: Identifying Relationship lending in the interbank market: a network approach. J. Bank Financ. 97, 20–36 (2018)

    Article  Google Scholar 

  4. León, C., Machado, C., Sarmiento, M.: Identifying central bank liquidity super-spreaders in interbank funds networks. J. Financ. Stab. 35, 75–92 (2018)

    Article  Google Scholar 

  5. Li, S., Sui, X., Xu, T.: Loss distribution of interbank contagion risk. Appl. Econ. Lett. 22(10), 830 (2015)

    Article  Google Scholar 

  6. Hübsch, A., Walther, U.: The impact of network inhomogeneities on contagion and system stability. Ann. Oper. Res. 254(1–2), 61–87 (2017)

    Article  MathSciNet  Google Scholar 

  7. Georg, C-P.: Contagious herding and endogenous network formation in financial networks. Working Paper, vol. 1700. European Central Bank (2014)

    Google Scholar 

  8. Fricke, D., Lux, T.: Core-periphery structure in the overnight money market: evidence from the e-MID trading platform. Comput. Econ. 45(3), 359–395 (2015)

    Article  Google Scholar 

  9. Gürcan, Ö.: On using agent-based modeling and simulation for studying blockchain systems. In: JFMS 2020-Journées Francophones de la Modélisation et de la Simulation. Cargèse, France (2020). Last accessed 3 Nov 2020

    Google Scholar 

  10. Eduardo, L., Hern, C.: On distributed artificial intelligence. Knowl. Eng. Rev. 3(1), 21–57 (1988)

    Article  Google Scholar 

  11. Hewitt, C., Inman, J.: DAI betwixt and between: from ’intelligent agents’ to open systems science. IEEE T Syst. Man. Cy. 21(6), 1409–1419 (1991)

    Article  Google Scholar 

  12. Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Auton. Agents Multi-Agent Syst. 1(1), 7–38 (1998)

    Article  Google Scholar 

  13. Ferber, J., Weiss, G.: Multi-agent systems: an introduction to distributed artificial intelligence, vol. 1. Addison-Wesley Reading (1999)

    Google Scholar 

  14. Georgeff, M., Pell, B., Pollack, M., Tambe, M., Wooldridge, M.: The belief-desire-intention model of agency. In: International Workshop on Agent Theories, Architectures, and Languages, pp. 1–10. Springer (1998)

    Google Scholar 

  15. Liu, A., Mo, C.Y.J., Paddrik, M.E., Yang, S.Y.: An agent-based approach to interbank market lending decisions and risk implications. Information 9(6) (2018)

    Google Scholar 

  16. Liu, A., Paddrik, M., Yang, S.Y., Zhang, X.: Interbank contagion: an agent-based model approach to endogenously formed networks. J. Bank Financ. 112, 105191 (2020)

    Article  Google Scholar 

  17. Yu, T., Lin, Z., Tang, Q.: Blockchain: the introduction and its application in financial accounting. J. Corp. Account. Financ. 29(4), 37–47 (2018)

    Article  Google Scholar 

  18. Pesch, P.J., Sillaber, C.: Distributed ledger, joint control?–blockchains and the GDPR’s transparency requirements. Comput. Law Rev. Int. 18(6) (2018)

    Google Scholar 

  19. Barroso, R.V., Lima, J.I.A.V., Lucchetti, A.H., Cajueiro, D.O.: Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning. J. Netw. Theory Financ. 2(4), 53–86 (2016)

    Google Scholar 

  20. Haber, G.: Optimal monetary policy responses to the financial crisis in the context of a macroeconomic agent-based model with dynamic expectations. Paper presented at the Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie, Frankfurt a. M., (2010)

    Google Scholar 

  21. Gurgone, A., Iori, G., Jafarey, S.: The effects of interbank networks on efficiency and stability in a macroeconomic agent-based model. J. Econ. Dyn. Control 91, 257–288 (2018)

    Article  MathSciNet  Google Scholar 

  22. Hałaj, G.: System-wide implications of funding risk. Phys. A Stat. Mech. Appl. 503, 1151–1181 (2018)

    Article  Google Scholar 

  23. Calimani, S., Hałaj, G., Żochowski, D.: Simulating fire sales in a system of banks and asset managers. J. Bank Financ.105707 (2019)

    Google Scholar 

  24. Gurgone, A., Iori, G.: A multi-agent methodology to assess the effectiveness of alternative systemic risk adjusted capital requirements. Discussion Paper, vol. 19/05 (2019)

    Google Scholar 

  25. Popoyan, L., Napoletano, M., Roventini, A.: Winter is possibly not coming: mitigating financial instability in an agent-based model with interbank market. J. Econ. Dyn. Control 117 (2020)

    Google Scholar 

  26. Georg, C.-P.: The effect of the interbank network structure on contagion and common shocks. J. Bank Financ. 37(7), 2216–2228 (2013)

    Article  Google Scholar 

  27. Iori, G., Mantegna, R.N., Marotta, L., Micciche, S., Porter, J., Tumminello, M.: Networked relationships in the E-MID interbank market: a trading model with memory. J. Econ. Dyn. Control 50, 98–116 (2015)

    Article  MathSciNet  Google Scholar 

  28. Smaga, P., Wilinski, M., Ochnicki, P., Arendarski, P., Gubiec, T.: Can banks default overnight? modelling endogenous contagion on the O/N interbank market. Quant. Financ. 18(11), 1815–1829 (2018)

    Article  MathSciNet  Google Scholar 

  29. Galbiati, M., Soramaki, K.: A competitive multi-agent model of interbank payment systems (2007). arXiv:07053050

  30. Rocha-Mier, L., Sheremetov, L., Villarreal, F.: Collective intelligence in multiagent systems: interbank payment systems application. In: Perception-based Data Mining and Decision Making in Economics and Finance, pp. 331–351. Springer (2007)

    Google Scholar 

  31. Hedjazi, B., Ahmed-Nacer, M., Aknine, S., Benatchba, K.: Multi-agent liquidity risk management in an interbank net settlement system. In: International Conference on Active Media Technology, pp. 103–114. Springer (2012)

    Chapter  Google Scholar 

  32. Ladley, D.: Contagion and risk-sharing on the inter-bank market. J. Econ. Dyn. Control 37(7), 1384–1400 (2013)

    Article  MathSciNet  Google Scholar 

  33. Bogg, P., Beydoun, G., Low, G.: When to use a multi-agent system? In: Pacific Rim International Conference on Multi-agents, pp. 98–108. Springer (2008)

    Google Scholar 

  34. Yang, T., Liu, Y., Yang, X., Kang, Y.: A Blockchain based smart agent system architecture. In: 4th International Conference on Crowd Science and Engineering, pp. 33–39 (2019)

    Google Scholar 

  35. Norling, E.: Capturing the quake player: using a bdi agent to model human behaviour. In: Second International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1080–1081 (2003)

    Google Scholar 

  36. Adam, C., Gaudou, B.: BDI agents in social simulations: a survey. Knowl. Eng. Rev. 31(3), 207–238 (2016)

    Article  Google Scholar 

  37. Chin, K.O., Gan, K.S., Alfred, R., Anthony, P., Lukose, D.: Agent architecture: an overviews. Trans. Sci. Technol. 1(1), 18–35 (2014)

    Google Scholar 

  38. Rao, A.S., Georgeff, M.P.: Decision procedures for BDI logics. J. Log. Comput. 8(3), 293–343 (1998)

    Article  MathSciNet  Google Scholar 

  39. Guerra-Hernández, A., El Fallah-Seghrouchni, A., Soldano, H.: Learning in BDI multi-agent systems. In: International Workshop on Computational Logic in Multi-agent Systems, pp. 218–233. Springer (2004)

    Google Scholar 

  40. Guerra-Hernández, A., Ortiz-Hernández, G., Luna-Ramírez, W.A.: Jason smiles: incremental BDI MAS learning. In: Sixth Mexican International Conference on Artificial Intelligence, Special Session, pp. 61–70. IEEE (2007)

    Google Scholar 

  41. Ahmed, M., Sriram, A., Singh, S.: Short term firm-specific stock forecasting with BDI framework. Comput. Econ. 55(3), 745–778 (2020)

    Article  Google Scholar 

  42. Singh, D., Sardina, S., Padgham, L., James, G.: Integrating learning into a BDI agent for environments with changing dynamics. In: 22th International Joint Conference on Artificial Intelligence (2011)

    Google Scholar 

  43. Gürcan, Ö.: Multi-agent modelling of fairness for users and miners in blockchains. In: International Conference on Practical Applications of Agents and Multi-agent Systems, pp. 92–99. Springer (2019)

    Google Scholar 

  44. Mbarek, B., Jabeur, N., Pitner, T., Yasar, A.-U.-H.: Empowering communications in vehicular networks with an intelligent blockchain-based solution. Sustainability 12(19), 7917 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This research is funded by the Initiative d’excellence (Idex) Université Grenoble Alpes, under grant C7H-LXP11A95-IRSMMI, and conducted at the Centre d’Etudes et de Recherches Appliquées à la Gestion (CERAG) in collaboration with the Laboratoire d’Informatique de Grenoble (LIG).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Morteza Alaeddini .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alaeddini, M., Dugdale, J., Reaidy, P., Madiès, P., Gürcan, Ö. (2021). An Agent-Oriented, Blockchain-Based Design of the Interbank Money Market Trading System. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2021. Smart Innovation, Systems and Technologies, vol 241. Springer, Singapore. https://doi.org/10.1007/978-981-16-2994-5_1

Download citation

Publish with us

Policies and ethics