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Modeling uncertainty in banking networks

Modeling uncertainty in banking networks
Modeling uncertainty in banking networks

Recent evidence from simulations of banking networks suggests that properties of the network design such as connectivity, bank size, or concentration affect networks' ability to withstand stress (Arinaminpathy et al. 2012; Gai at al. 2011). However, those studies typically assume that all banks have complete knowledge about the whole system. Here we introduce uncertainty into what banks know about other banks. We model uncertainty scenarios in which uncertainty is translated into asymmetric distribution of information among banks relative to the proximity of information source. Instead of knowing everything agents are faced with information delay and limited information availability. We show that when uncertainty is introduced, the system becomes more fragile to sudden shocks and a relatively small distress can push the system over the tipping point in which the whole banking network collapses.

Banking networks, Simulation, Uncertainty
2194-5357
107-113
Springer
Davidovic, Stojan
5825c753-1e8f-42dd-bc3e-312f72c33c1d
Galesic, Mirta
c2fa14e9-d849-426b-a180-16df684ae86c
Katsikopoulos, Konstantinos
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Arinaminpathy, Nimalan
41ab9c51-5636-456a-bb07-c162c5453a85
Davidovic, Stojan
5825c753-1e8f-42dd-bc3e-312f72c33c1d
Galesic, Mirta
c2fa14e9-d849-426b-a180-16df684ae86c
Katsikopoulos, Konstantinos
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Arinaminpathy, Nimalan
41ab9c51-5636-456a-bb07-c162c5453a85

Davidovic, Stojan, Galesic, Mirta, Katsikopoulos, Konstantinos and Arinaminpathy, Nimalan (2014) Modeling uncertainty in banking networks. In Distributed Computing and Artificial Intelligence, 11th International Conference, DCAI 2014. vol. 290, Springer. pp. 107-113 . (doi:10.1007/978-3-319-07593-8_14).

Record type: Conference or Workshop Item (Paper)

Abstract

Recent evidence from simulations of banking networks suggests that properties of the network design such as connectivity, bank size, or concentration affect networks' ability to withstand stress (Arinaminpathy et al. 2012; Gai at al. 2011). However, those studies typically assume that all banks have complete knowledge about the whole system. Here we introduce uncertainty into what banks know about other banks. We model uncertainty scenarios in which uncertainty is translated into asymmetric distribution of information among banks relative to the proximity of information source. Instead of knowing everything agents are faced with information delay and limited information availability. We show that when uncertainty is introduced, the system becomes more fragile to sudden shocks and a relatively small distress can push the system over the tipping point in which the whole banking network collapses.

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More information

Published date: 1 January 2014
Venue - Dates: 11th International Symposium on Distributed Computing and Artificial Intelligence 2014, DCAI 2014, , Salamanca, Spain, 2014-06-04 - 2014-06-06
Keywords: Banking networks, Simulation, Uncertainty

Identifiers

Local EPrints ID: 440694
URI: http://eprints.soton.ac.uk/id/eprint/440694
ISSN: 2194-5357
PURE UUID: ad51ddc4-e485-4928-90ef-e4d0db5c181d
ORCID for Konstantinos Katsikopoulos: ORCID iD orcid.org/0000-0002-9572-1980

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Date deposited: 13 May 2020 16:36
Last modified: 10 Jun 2022 01:47

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Contributors

Author: Stojan Davidovic
Author: Mirta Galesic
Author: Nimalan Arinaminpathy

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