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



Are we forecasting:

Timing, , or Outcome?

or Macro events?

Process

Micro



• Three components of models

– Topology of interactions

– System dynamics

– Economic behavior

•

How to bring research to policy?

• Financial Network Analytics -software

Outline



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.



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.



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


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



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



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



Complex dynamics

Bank i Bank j

Payment system

1 Agent instructs

bank to send a

payment


is debited

Di D j

5 Payment account

is credited

4 Payment account

is debited

Productive Agent Productive Agent


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



• 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



Liquidity demand curve



How to operationalize all this?



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



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.



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)



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



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



Scope

Research Policy Operations



Paradigm

Datavalidation

Analysis andmodeling

Visualization



demo



www.fna.fi



Thank you

Contact us

[email protected]

mailto:[email protected]

mailto:[email protected]

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