Tools for Forecasting Financial Crisis @ ETH
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The document discusses tools and methodologies for forecasting financial crises, emphasizing the need for understanding complex socio-economic systems. It highlights the importance of financial network analytics and the dynamics of payment systems while addressing the challenges of data analysis in regulatory contexts. Key concepts include liquidity choices among banks, network mapping of financial institutions, and the integration of technology for effective policy-making.
Tools for Forecasting Financial Crisis @ ETH
- 1. Presentation at ‘Forecasting financial crisis’ topical sesson at ETH Workshop on ‘Coping with Crisis in Complex Socio-Economic Systems’23 June 2011Tools for forecasting financial crisisKimmo SoramäkiSoramaki Networks Oykimmo@soramaki.net
- 2. Are we forecasting:Timing, , or Outcome? or Macro events?ProcessMicro
- 3. Three components of modelsTopology of interactionsSystem dynamicsEconomic behaviorHow to bring research to policy?Financial Network Analytics -softwareOutline
- 4. Paymentsystems ~1939 tr ~194 tr ~120 tr ~5 tr Bech, Preisig and Soramäki (2008), FRBNY Economic Review, Vol. 14, No. 2.
- 5. Topology of interactionsDegree distributionTotal of ~8000 banks66 banks comprise 75% of value25 banks completely connectedSoramäki, Bech, Beyeler, Glass and Arnold (2006), Physica A, Vol. 379, pp 317-333.
- 6. Complex dynamics4 Payment account is debited5 Payment account is creditedBiBj6 Depositor account is credited3 Payment is settled or queuedDiDjQiBi > 0Qj > 0Qj7 Queued payment, if any, is releasedProductive AgentProductive AgentCentral bankPayment systemLiquidityMarketBank iBank j2 Depositor account is debited1 Agent instructs bank to send a paymentBeyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
- 7. PaymentSystemInstructionsPaymentsLiquiditySummed over the network, instructions arrive at a steady rateWhen liquidity is high payments are submitted promptly and banks process payments independently of each other
- 8. PaymentSystemInstructionsPaymentsLiquidityReducing liquidity leads to episodes of congestion when queues build, and cascades of settlement activity when incoming payments allow banks to work off queues. Payment processing becomes coupled across the network
- 9. Complex dynamics4 Payment account is debited5 Payment account is creditedBiBj6 Depositor account is credited3 Payment is settled or queuedDiDjQiBi > 0Qj > 0Qj7 Queued payment, if any, is releasedProductive AgentProductive AgentCentral bankPayment systemLiquidityMarketBank iBank j2 Depositor account is debited1 Agent instructs bank to send a paymentBeyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
- 10. Example: How much liquidity to post?Cost for a bank in a payment system depends on Choice of liquidity and Delays of settlementBanks liquidity choice depends on other banks’ liquidity choice We develop ABM payoffs determined by a realistic settlement process reinforcement learninglook at equilibrium Economic behaviorGalbiatiand Soramäki (2011), JEDC, Vol. 35, Iss. 6, pp 859-875
- 12. How to operationalize all this?
- 13. Data tsunamiDigital information is doubling every 1.2 years. Open data, data science, …Regulatory response to recent financial crisis was to strengthen macro-prudential supervision with mandates for more regulatory dataThe challenge will be to understand and analyze the data“Analytics based policy”, i.e. the application of computer technology, operational research,and statistics to solve regulatory problemsKatsushika Hokusai. The great wave off Kanagawa ~1830
- 14. Network mapsRecent financial crisis brought to light the need to look at links between financial institutionsNatural way to visualize the financial system‘Network thinking’ widespread by regulatorsMapping of the financial system has only begunEratosthenes' map of the known world, c.194 BC.
- 15. IntelligenceFinancial crisis are different and rare Technology, products and practices changeData is not clean, actions are not ‘rational’Hard to develop algorithmsA solution is to augment human intelligence (in contrast to AI and algorithms)
- 17. ObjectivesProvide a tool for exploration, analysis and visualization of regulatory financial data Provide a extendible platform for custom functionality, and agent based and simulation modelsMake advances in research available to policy
- 18. Roots of the workBof-PSS2Bank of Finland, 1997-Payment system simulator used in ~60 central banksLokiNISAC at Sandia National Laboratories, 2004-Toolkit for network analysis and ABMSponsored by Norges Bank, collaborative efforts with other central banks
- 19. Scope
- 20. Paradigm
- 21. demo
- 22. www.fna.fi