Network Simulations for Business Continuity
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The document discusses network simulations for business continuity in financial systems, presented by Dr. Kimmo Soramäki at the Swift Operations Forum. It highlights the importance of analyzing network behavior for predicting liquidity issues and operational disruptions among banks, supported by historical examples of payment system simulations. Furthermore, it addresses the role of network topology in understanding financial networks and modeling liquidity flows in times of stress.
Network Simulations for Business Continuity
- 1. Amsterdam, 28 November 2013 SWIFT Operations Forum Network Simulations for Business Continuity Dr. Kimmo Soramäki Founder and CEO Financial Network Analytics www.fna.fi
- 2. Network Simulation – Interactive Demo Failure Scenario Black node = can receive but cannot send Normal Scenario Green node = Liquidity available Red node = No, liquidity. Queues build up. 2
- 3. Real Networks: Fedwire Payment Network ‘Furball’ 3
- 4. Fedwire Core Fedwire Interbank Payment Network Fall 2001 Around 8000 banks, 66 banks comprise 75% of value,25 banks completely connected Soramäki, Bech, Beyeler, Glass and Arnold (2007), Physica A, Vol. 379, pp 317-333. 4
- 7. More Network Maps Federal funds Bech, M.L. and Atalay, E. (2008), “The Topology of the Federal Funds Market”. ECB Working Paper No. 986. Italian money market Iori G, G de Masi, O Precup, G Gabbi and G Caldarelli (2008): “A network analysis of the Italian overnight money market”, Journal of Economic Dynamics and Control, vol. 32(1), pages 259-278 Unsecured Sterling money market Wetherilt, A. P. Zimmerman, and K. Soramäki (2008), “The sterling unsecured loan market during 2006–2008: insights from network topology“, in Leinonen (ed), BoF Scientific monographs, E 42 Cross-border bank lending Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global banking:1978-2009. IMF Working Paper WP/11/74. 7
- 8. Network Theory Financial Network Analysis Social Network Analysis Network Science NETWORK THEORY Graph & Matrix Theory Computer Science Biological Network Analysis
- 9. Structure of links between nodes matters The performance of a node (bank) cannot be analyzed on the basis its own properties and behavior alone To understand the performance of one node (bank), one must analyze the behavior of nodes that may be several links apart in the network. Each affect each. 9
- 10. Networks Brings us Beyond the Data Cube For example: Entities: 100 banks Variables: Liquidity, Opening Balance, … Time: Daily data Links: Bilateral payment flows Links are the 4th dimension to data Information on the links allows us to develop better models for banks' liquidity situation in times of stress 10
- 12. Predictive Modeling • Predictive modeling is the process by which a model is created to try to best predict the probability of an outcome • “What is the impact if a large bank has an operational disruption at noon?” – Who is affected first? – Who is affected most? – What is the impact on my bank in an hour? • Valuable information for decision making 12
- 13. Short History of Payment System Simulations • 1997 : Bank of Finland – Evaluate liquidity needs of banks when Finland’s RTGS system was joined with TARGET – See Koponen-Soramaki (1998) “Liquidity needs in a modern interbank payment systems: • 2000 : Bank of Japan and FRBNY – Test features for BoJ-Net/Fedwire • 2001 - : CLS approval process and ongoing oversight – Test CLS risk management – Evaluate settlement’ members capacity for pay-ins – Understand how the system works • Since: Bank of Canada, Banque de France, Nederlandsche Bank, Norges Bank, TARGET2, and many others • 2010 - : Bank of England new CHAPS – Evaluate alternative liquidity saving mechanisms – Use as platform for discussions with banks – Denby-McLafferty (2012) “Liquidity Saving in CHAPS: A Simulation Study”
- 14. Stress Testing Basel Committee for Banking Supervision published in April 2013 document “Monitoring Tools for Intraday Liquidity Management”. It outlines stress scenarios, one of which is: “Counterparty stress: a major counterparty suffers an intraday stress event which prevents it from making payments “ Stress Simulation Demo 14
- 15. Thank you Blog, library and demos are available at www.fna.fi Dr. Kimmo Soramäki kimmo@fna.fi Twitter: soramaki 15