Tools for Forecasting Financial Crisis
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Tools for forecasting financial crisis
Kimmo Soramäki
Soramaki Networks [email protected]
Presentation at „Forecasting financial crisis‟ topical sesson at ETH Workshop on „Copingwith Crisis in Complex Socio-EconomicSystems‟
23 June 2011
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Are we forecasting:
Timing, , or Outcome?
or Macro events?
Process
Micro
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• Three components of models
– Topology of interactions
– System dynamics
– Economic behavior
•
How to bring research to policy?
• Financial Network Analytics -software
Outline
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Payment systems
0.00E+00
5.00E+14
1.00E+15
1.50E+15
2.00E+15
2.50E+15
Annual value (euros) Liquidity need Age of the universe (hours)
0.00E+00
5.00E+14
1.00E+15
1.50E+15
2.00E+15
2.50E+15
Annual value (euros) Liquidity need
0.00E+00
5.00E+14
1.00E+15
1.50E+15
2.00E+15
2.50E+15
Annual value (euros)
0.00E+00
5.00E+14
1.00E+15
1.50E+15
2.00E+15
2.50E+15
Annual value (euros) Liquidity need Age of the universe (days)
~1939 tr
~194 tr~120 tr~5 tr
Bech, Preisig and Soramäki (2008),FRBNY Economic Review, Vol. 14, No. 2.
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Topology of interactions
Total of ~8000 banks
66 banks comprise 75% of value25 banks completely connected
Degree distribution
Soramäki, Bech, Beyeler, Glass and Arnold(2006), Physica A, Vol. 379, pp 317-333.
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Complex dynamics
Bank i Bank j
Payment system
1 Agent instructs
bank to send a
payment
2 Deposi tor account
is debited
Di D j
5 Payment account
is credited
4 Payment account
is debited
Productive Agent Productive Agent
6 Deposi tor account
is credited
Qi
3 Payment is s ett led
or queued
Bi > 0 Q j
7 Queued payment,
if any, is released
Q j > 0
Bi B j
Central bank
Beyeler, Glass, Bech and Soramäki(2007), Physica A, 384-2, pp 693-718.
Liquidity
Market
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Instructions
P a y m e n t s
TimeTime
PaymentSystem
When liquidity is highpayments are submittedpromptly and banksprocess paymentsindependently of each
other
Instructions Payments
Summed overthe network,instructionsarrive at a steadyrate
Liquidity
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Instructions
P a y m e n t s
Time
Reducing liquidity leads toepisodes of congestionwhen queues build, andcascades of settlementactivity when incomingpayments allow banks to
work off queues. Paymentprocessing becomescoupled across thenetwork
PaymentSystem
Instructions Payments
Time
Cascade Length
F r e q u e n c y
Liquidity
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Complex dynamics
Bank i Bank j
Payment system
1 Agent instructs
bank to send a
payment
2 Deposi tor account
is debited
Di D j
5 Payment account
is credited
4 Payment account
is debited
Productive Agent Productive Agent
6 Deposi tor account
is credited
Qi
3 Payment is s ett led
or queued
Bi > 0 Q j
7 Queued payment,
if any, is released
Q j > 0
Bi B j
Central bank
Beyeler, Glass, Bech and Soramäki(2007), Physica A, 384-2, pp 693-718.
Liquidity
Market
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• 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 859-875
Economic behavior
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Liquidity demand curve
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How to operationalize all this?
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Data tsunami
• Digital information is doubling every 1.2 years. Open data, datascience, …
• Regulatory response to recent financial crisis was to strengthen
macro-prudential supervision with mandates for more regulatorydata
• The challenge will be tounderstand and analyze the data
• “ Analytics based policy”, i.e. theapplication of computertechnology, operational research,and statistics to solve regulatoryproblems
Katsushika Hokusai. The great wave off Kanagawa ~1830
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Network maps
• Recent financial crisis brought to light the need to look atlinks between financial institutions
• Natural way to visualize the financial system
• „Network thinking‟ widespread by regulators
• Mapping of the financial systemhas only begun
Eratosthenes' map of the knownworld, c.194 BC.
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Intelligence
• Financial crisis are different andrare
• Technology, products andpractices change
• Data is not clean, actions are not „rational‟
• Hard to develop algorithms
• A solution is to augment humanintelligence(in contrast to AI and algorithms)
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Objectives
• Provide a tool for exploration, analysis andvisualization of regulatory financial data
• Provide a extendible platform for customfunctionality, and agent based and simulationmodels
• Make advances in research available to policy
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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 effortswith other central banks
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Scope
Research Policy Operations
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Paradigm
Datavalidation
Analysis andmodeling
Visualization
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demo
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www.fna.fi
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