Your SlideShare is downloading. ×
Tools for Forecasting Financial Crisis @ ETH
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Tools for Forecasting Financial Crisis @ ETH

1,029
views

Published on

Presentation at the topical session "Forecasting Financial Crisis" at ETH workshop on "Coping with Crises in Complex Socio-Economic Systems" on 23 June 2011.

Presentation at the topical session "Forecasting Financial Crisis" at ETH workshop on "Coping with Crises in Complex Socio-Economic Systems" on 23 June 2011.

Published in: Economy & Finance, Business

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,029
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
33
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Presentation at ‘Forecasting financial crisis’ topical sesson at ETH Workshop on ‘Coping with Crisis in Complex Socio-Economic Systems’
    23 June 2011
    Tools for forecasting financial crisis
    Kimmo Soramäki
    Soramaki Networks Oy
    kimmo@soramaki.net
  • 2. Are we forecasting:
    Timing, , or Outcome?
    or Macro events?
    Process
    Micro
  • 3. Three components of models
    Topology of interactions
    System dynamics
    Economic behavior
    How to bring research to policy?
    Financial Network Analytics -software
    Outline
  • 4. Paymentsystems
    ~1939 tr
    ~194 tr
    ~120 tr
    ~5 tr
    Bech, Preisig and Soramäki (2008), FRBNY Economic Review, Vol. 14, No. 2.
  • 5. Topology of interactions
    Degree distribution
    Total of ~8000 banks66 banks comprise 75% of value25 banks completely connected
    Soramäki, Bech, Beyeler, Glass and Arnold (2006), Physica A, Vol. 379, pp 317-333.
  • 6. Complex dynamics
    4 Payment account is debited
    5 Payment account is credited
    Bi
    Bj
    6 Depositor account is credited
    3 Payment is settled or queued
    Di
    Dj
    Qi
    Bi > 0
    Qj > 0
    Qj
    7 Queued payment, if any, is released
    Productive Agent
    Productive Agent
    Central bank
    Payment system
    Liquidity
    Market
    Bank i
    Bank j
    2 Depositor account is debited
    1 Agent instructs bank to send a payment
    Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
  • 7. Payment
    System
    Instructions
    Payments
    Liquidity
    Summed over the network, instructions arrive at a steady rate
    When liquidity is high payments are submitted promptly and banks process payments independently of each other
  • 8. Payment
    System
    Instructions
    Payments
    Liquidity
    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
  • 9. Complex dynamics
    4 Payment account is debited
    5 Payment account is credited
    Bi
    Bj
    6 Depositor account is credited
    3 Payment is settled or queued
    Di
    Dj
    Qi
    Bi > 0
    Qj > 0
    Qj
    7 Queued payment, if any, is released
    Productive Agent
    Productive Agent
    Central bank
    Payment system
    Liquidity
    Market
    Bank i
    Bank j
    2 Depositor account is debited
    1 Agent instructs bank to send a payment
    Beyeler, Glass, Bech and Soramäki (2007), Physica A, 384-2, pp 693-718.
  • 10. 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
    Economic behavior
    Galbiatiand Soramäki (2011), JEDC, Vol. 35, Iss. 6, pp 859-875
  • 11. Liquidity demand curve
  • 12. How to operationalize all this?
  • 13. 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
  • 14. 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.
  • 15. 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)
  • 16.
  • 17. 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
  • 18. 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
  • 19. Scope
  • 20. Paradigm
  • 21. demo
  • 22. www.fna.fi
  • 23. Thank you
    Contact us
    kimmo.soramaki@fna.fi