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.
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Notes
- 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.
How Blockchain Could Disrupt Banking, https://www.cbinsights.com/research/blockchain-disrupting-banking/, last access on 21/02/2021.
- 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
Acemoglu, D., Ozdaglar, A., Tahbaz-Salehi, A.: Systemic risk and stability in financial networks. Am. Econ. Rev. 105(2), 564–608 (2015)
Leventides, J., Loukaki, K., Papavassiliou, V.G.: Simulating financial contagion dynamics in random interbank networks. J. Econ. Behav. Organ. 158, 500–525 (2019)
Kobayashi, T., Takaguchi, T.: Identifying Relationship lending in the interbank market: a network approach. J. Bank Financ. 97, 20–36 (2018)
León, C., Machado, C., Sarmiento, M.: Identifying central bank liquidity super-spreaders in interbank funds networks. J. Financ. Stab. 35, 75–92 (2018)
Li, S., Sui, X., Xu, T.: Loss distribution of interbank contagion risk. Appl. Econ. Lett. 22(10), 830 (2015)
Hübsch, A., Walther, U.: The impact of network inhomogeneities on contagion and system stability. Ann. Oper. Res. 254(1–2), 61–87 (2017)
Georg, C-P.: Contagious herding and endogenous network formation in financial networks. Working Paper, vol. 1700. European Central Bank (2014)
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)
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
Eduardo, L., Hern, C.: On distributed artificial intelligence. Knowl. Eng. Rev. 3(1), 21–57 (1988)
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)
Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Auton. Agents Multi-Agent Syst. 1(1), 7–38 (1998)
Ferber, J., Weiss, G.: Multi-agent systems: an introduction to distributed artificial intelligence, vol. 1. Addison-Wesley Reading (1999)
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)
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)
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)
Yu, T., Lin, Z., Tang, Q.: Blockchain: the introduction and its application in financial accounting. J. Corp. Account. Financ. 29(4), 37–47 (2018)
Pesch, P.J., Sillaber, C.: Distributed ledger, joint control?–blockchains and the GDPR’s transparency requirements. Comput. Law Rev. Int. 18(6) (2018)
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)
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)
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)
Hałaj, G.: System-wide implications of funding risk. Phys. A Stat. Mech. Appl. 503, 1151–1181 (2018)
Calimani, S., Hałaj, G., Żochowski, D.: Simulating fire sales in a system of banks and asset managers. J. Bank Financ.105707 (2019)
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)
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)
Georg, C.-P.: The effect of the interbank network structure on contagion and common shocks. J. Bank Financ. 37(7), 2216–2228 (2013)
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)
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)
Galbiati, M., Soramaki, K.: A competitive multi-agent model of interbank payment systems (2007). arXiv:07053050
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)
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)
Ladley, D.: Contagion and risk-sharing on the inter-bank market. J. Econ. Dyn. Control 37(7), 1384–1400 (2013)
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)
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)
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)
Adam, C., Gaudou, B.: BDI agents in social simulations: a survey. Knowl. Eng. Rev. 31(3), 207–238 (2016)
Chin, K.O., Gan, K.S., Alfred, R., Anthony, P., Lukose, D.: Agent architecture: an overviews. Trans. Sci. Technol. 1(1), 18–35 (2014)
Rao, A.S., Georgeff, M.P.: Decision procedures for BDI logics. J. Log. Comput. 8(3), 293–343 (1998)
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)
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)
Ahmed, M., Sriram, A., Singh, S.: Short term firm-specific stock forecasting with BDI framework. Comput. Econ. 55(3), 745–778 (2020)
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)
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)
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)
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).
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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
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