Presentation at the topical session "Forecasting Financial Crisis" at ETH workshop on "Coping with Crises in Complex Socio-Economic Systems" on 23 June 2011.
1. Presentation at ‘Forecasting financial crisis’ topical sesson at ETH Workshop on ‘Coping with Crisis in Complex Socio-Economic Systems’ 23 June 2011 Tools for forecasting financial crisis Kimmo Soramäki Soramaki Networks Oy kimmo@soramaki.net
3. Three components of models Topology of interactions System dynamics Economic behavior How to bring research to policy? Financial Network Analytics -software Outline
5. Topology of interactions Degree distribution Total of ~8000 banks66 banks comprise 75% of value25 banks completely connected Soramäki, Bech, Beyeler, Glass and Arnold (2006), Physica A, Vol. 379, pp 317-333.
6. Complex dynamics 4 Payment account is debited 5 Payment account is credited Bi Bj 6 Depositor account is credited 3 Payment is settled or queued Di Dj Qi Bi > 0 Qj > 0 Qj 7 Queued payment, if any, is released Productive Agent Productive Agent Central bank Payment system Liquidity Market Bank i Bank j 2 Depositor account is debited 1 Agent instructs bank to send a payment Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
7. Payment System Instructions Payments Liquidity Summed over the network, instructions arrive at a steady rate When liquidity is high payments are submitted promptly and banks process payments independently of each other
8. Payment System Instructions Payments Liquidity Reducing 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 dynamics 4 Payment account is debited 5 Payment account is credited Bi Bj 6 Depositor account is credited 3 Payment is settled or queued Di Dj Qi Bi > 0 Qj > 0 Qj 7 Queued payment, if any, is released Productive Agent Productive Agent Central bank Payment system Liquidity Market Bank i Bank j 2 Depositor account is debited 1 Agent instructs bank to send a payment Beyeler, 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 settlement Banks liquidity choice depends on other banks’ liquidity choice We develop ABM payoffs determined by a realistic settlement process reinforcement learning look at equilibrium Economic behavior Galbiatiand Soramäki (2011), JEDC, Vol. 35, Iss. 6, pp 859-875
13. Data tsunami Digital 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 data The 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 problems Katsushika Hokusai. The great wave off Kanagawa ~1830
14. Network maps Recent financial crisis brought to light the need to look at links between financial institutions Natural way to visualize the financial system ‘Network thinking’ widespread by regulators Mapping of the financial system has only begun Eratosthenes' map of the known world, c.194 BC.
15. Intelligence Financial crisis are different and rare Technology, products and practices change Data is not clean, actions are not ‘rational’ Hard to develop algorithms A solution is to augment human intelligence (in contrast to AI and algorithms)
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17. Objectives Provide a tool for exploration, analysis and visualization of regulatory financial data Provide a extendible platform for custom functionality, and agent based and simulation models Make advances in research available to policy
18. Roots of the work Bof-PSS2 Bank of Finland, 1997- Payment system simulator used in ~60 central banks Loki NISAC at Sandia National Laboratories, 2004- Toolkit for network analysis and ABM Sponsored by Norges Bank, collaborative efforts with other central banks