Tools for Forecasting Financial Crisis @ ETH

Tools for forecasting financial crisis

Kimmo SoramäkiSoramaki Networks Oykimmo@soramaki.net

Presentation at ‘Forecasting financial crisis’ topical sesson at ETH Workshop on ‘Coping with Crisis in Complex Socio-Economic Systems’

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

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 need0.00E+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)0.00E+00

5.00E+14

1.00E+15

1.50E+15

2.00E+15

2.50E+15

~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 banks66 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 Depositor account is debited

Di Dj

5 Payment account is credited

4 Payment account is debited

Productive Agent Productive Agent

6 Depositor account is credited

Qi

3 Payment is settled or queued

Bi > 0 Qj

7 Queued payment, if any, is released

Qj > 0

Bi Bj

Central bank

Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.

LiquidityMarket

5 5 0 0

5 6 0 0

5 7 0 0

5 8 0 0

5 9 0 0

6 0 0 0

6 1 0 0

5 5 0 0 5 7 0 0 5 9 0 0 6 1 0 0

Instructions

Pay

men

ts

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 1 6 0 0 1 8 0 0 2 0 0 0

Time

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 1 6 0 0 1 8 0 0 2 0 0 0

Time

PaymentSystem

When liquidity is high payments are submitted promptly and banks process payments independently of each other

Instructions Payments

Summed over the network, instructions arrive at a steady rate

Liquidity

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

5 5 0 0 5 7 0 0 5 9 0 0 6 1 0 0

Instructions

Pay

men

ts

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 1 6 0 0 1 8 0 0 2 0 0 0

Time

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

PaymentSystem

Instructions Payments0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 1 6 0 0 1 8 0 0 2 0 0 0

Time

1 E -0 4

0 .0 0 1

0 .0 1

0 .1

1

1 1 0 1 0 0 1 0 0 0 1 0 0 0 0

Cascade Length

Fre

qu

ency

Liquidity

Complex dynamics

Bank i Bank j

Payment system

1 Agent instructs bank to send a payment

2 Depositor account is debited

Di Dj

5 Payment account is credited

4 Payment account is debited

Productive Agent Productive Agent

6 Depositor account is credited

Qi

3 Payment is settled or queued

Bi > 0 Qj

7 Queued payment, if any, is released

Qj > 0

Bi Bj

Central bank

Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.

LiquidityMarket

• 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, 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

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.

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)

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

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

Scope

Research Policy Operations

Paradigm

Data validation

Analysis and modeling Visualization

demo

www.fna.fi

Thank you

Contact uskimmo.soramaki@fna.fi