Abstract
This paper reviews some recent applications of flow network theory to the modelling of financial systems and of interbank liquidity networks. Three features of network flows have proven to be particularly useful in this field: i) the modularity of the transmission of flows across a network; ii) the constancy of a flow across all cuts of a nertwork; iii) the known ‘max flow - minimum cut’ theorem by Ford and Fulkerson. These properties of flow networks have been applied to evaluate the exposition to contagion of financial networks and the carrying capacity of interbank liquidity networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahuja, K.R., Magnanti, T.L., Orlin, J.B.: Network Flows: theory, algorithms and applications. Prentice Hall, New Jersey (1993)
Eboli, M.: The mechanics of direct contagion in financial systems: a flow network approach. Mimeo Previous Versions Presented at the Bank of England and at the National Bank of Denmark (2011)
Eboli, M.: Direct contagion in financial networks with mark-to-market and historical cost accounting rules. International Journal of Economics and Finance 2(5) (2010)
Eboli, M., Castiglionesi, F.: Liquidity transfers in interbank liquidity networks. Mimeo (2011)
Eisenberg, L., Noe, T.H.: Systemic risk in nancial systems. Management Science 47(2), 236–249 (2001)
Ford Jr., L.R., Fulkerson, D.R.: Maximal flow through a network. Canadian Journal of Mathematics 8, 339–404 (1956)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Eboli, M. (2013). Financial Applications of Flow Network Theory. In: Proto, A., Squillante, M., Kacprzyk, J. (eds) Advanced Dynamic Modeling of Economic and Social Systems. Studies in Computational Intelligence, vol 448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32903-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-32903-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32902-9
Online ISBN: 978-3-642-32903-6
eBook Packages: EngineeringEngineering (R0)