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Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
Financial Network Analytics @ Uni Delft
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Financial Network Analytics @ Uni Delft

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Presentation at Delft University's Mathematics and Computer Science department on Financial Networks, on analyzing and modeling them and on the www.fna.fi service.

Presentation at Delft University's Mathematics and Computer Science department on Financial Networks, on analyzing and modeling them and on the www.fna.fi service.

Published in: Economy & Finance, Business
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  • 1. Presentation at TU Delft2 November 2011Financial Network AnalyticsKimmo Soramäkikimmo@soramaki.net
  • 2. “When the crisis came, the serious limitations of existing economicand financial models immediately became apparent.[...]As a policy-maker during the crisis, I found the available models oflimited help. In fact, I would go further: in the face of the crisis, wefelt abandoned by conventional tools.” in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010
  • 3. We are talking about systemic risk ≠ systematic risk News articles mentioning “systemic risk”, Source: trends.google.com• The risk of disruption to a financial entity with spillovers to the real economy• Risk of a crisis that stresses key intermediation markets and leads to their breakdown, which impacts the broader economy and requires government intervention• Risk that critical nodes of a financial network cease to function as designed, disrupting linkages-> some chain of events that starts or gets magnified in the finance sector andmakes us all worse off
  • 4. Agenda• Three components of models – Topology of financial networks – System mechanics – Behavioral dynamics• How to bring research to policy?• Financial Network Analytics -software
  • 5. Payment systems 2.50E+15 ~1939 tr 2.00E+15 1.50E+15 1.00E+15 5.00E+14 ~194 tr ~120 tr 0.00E+00 ~5 tr Annual value (euros) Liquidity need Age of the universe (hours) Age of the universe (days) Bech, Preisig and Soramäki (2008), FRBNY Economic Review, Vol.
  • 6. Topology of interactions Degree distribution Total of ~8000 banks 66 banks comprise 75% of value Soramäki, Bech, Beyeler, Glass and Arnold 25 banks completely connected (2006), Physica A, Vol. 379, pp 317-333.
  • 7. System mechanics Central bank4 Payment account Payment system 5 Payment accountis debited is credited Bi Bj 6 Depositor account3 Payment is settled is creditedor queued Qi Bi > 0 Di Liquidity Dj Qj > 0 Qj Market2 Depositor account Bank i Bank j 7 Queuedis debited payment, if any, is released1 Agent instructsbank to send apayment Productive Agent Productive Agent Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
  • 8. Payment System Instructions Payments Time Liquidity TimeSummed overthenetwork, instructi When liquidity is highons arrive at a payments are submitted Paymentssteady rate promptly and banks process payments independently of each other Instructions
  • 9. Payment System Instructions Payments Liquidity Time TimeReducing liquidity leads toepisodes of congestionwhen queues build, andcascades of settlement Frequency Paymentsactivity when incomingpayments allow banks towork off queues. Paymentprocessing becomes Cascade Lengthcoupled across the Instructionsnetwork
  • 10. System mechanics Central bank4 Payment account Payment system 5 Payment accountis debited is credited Bi Bj 6 Depositor account3 Payment is settled is creditedor queued Qi Bi > 0 Di Liquidity Dj Qj > 0 Qj Market2 Depositor account Bank i Bank j 7 Queuedis debited payment, if any, is released1 Agent instructsbank to send apayment Productive Agent Productive Agent Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
  • 11. Economic behavior• 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 Galbiati and Soramäki (2011), JEDC, Vol. 35, Iss. 6, pp 859-
  • 12. Liquidity demand curve
  • 13. How to operationalize all this?
  • 14. 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
  • 15. 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.
  • 16. 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)
  • 17. Financial Network Analytics Platform
  • 18. Screen elements Access via browser Result panel shows Explain screen in intranet, internet or desktop command output And creates files (charts, data, etc) Each command has ‘Visualize’ screen Operation based different Submit command shows created on commands parameterscharts and layouts Switch between‘point-and-click’ and Files and database command line view connections are in file panels 18
  • 19. Tabs allow multiplevisualisations open All visualisations are html documents that work also outside FNA 19
  • 20. Dashboard (concept) The dashboard can combine multiple views to the data on a single screen It can be available e.g. on the intranet and 20 updated daily
  • 21. Command line All commands can be submitted using command syntax History provides an easy way to make new scripts for research or for the dashboard All commandssubmitted (also from point-and-click) are shown in history 21
  • 22. Command line Scripts can be run from the scripts panel or as regular jobs by the server 22
  • 23. Objectives • Provide a tool for exploration, analysis and visualization of regulatory financial data • Make online financial available for easy analysis • Provide a extendible platform for custom functionality, agent based models and other simulation models • Make advances in research available to policy
  • 24. FNA is available at www.fna.fi Contact us kimmo@soramaki.net
  • 25. Technical details• Performance – Client server architecture allows use of high performance servers, computer clusters and cloud computing – High-performance graph engine (neo4j.org) – Fast client application (Google web toolkit e.g. as in gmail.com)• Security – Sensitivity and confidentiality of data creates addition constraints for analysis – Data is stored on server where it can be protected better (vs analysts desktops) – Each user accesses FNA with her own account – SSL encryption of traffic – Logging and analytics, audit trail• Integration to corporate IT – Integration to databases possible – FNA accounts can be managed centrally by IT (integration to LDAP systems) – Can run on most application servers – Modular structure allows easier updates

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