tools for forecasting financial crisis @ eth
DESCRIPTION
Presentation at the topical session "Forecasting Financial Crisis" at ETH workshop on "Coping with Crises in Complex Socio-Economic Systems" on 23 June 2011.TRANSCRIPT
Tools for forecasting financial crisis
Kimmo SoramäkiSoramaki Networks [email protected]
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
