Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

New approaches for payment system simulation research


Published on

The presentation looks at the importance topology of interactions in payment system and the behavior of banks in the system

Published in: Economy & Finance, Business
  • Be the first to comment

  • Be the first to like this

New approaches for payment system simulation research

  1. 1. New approaches for payment system simulation research Kimmo Soramäki TKK, Helsinki, 3.9.2007
  2. 2. Payment systems <ul><li>All economic and financial activity necessitates payments </li></ul><ul><li>Payments need to be settled somehow </li></ul><ul><li>Payments can be intra-bank or interbank </li></ul><ul><ul><li>For the latter: need for a payment system </li></ul></ul><ul><li>Interbank payments account to ~3 trillion a day in US = 80 times the GDP on annual level </li></ul><ul><li>Efficient and safe interbank payment systems are important for </li></ul><ul><ul><li>Efficient financial markets </li></ul></ul><ul><ul><li>Financial stability </li></ul></ul><ul><ul><li>Monetary policy </li></ul></ul><ul><li>Settling payments requires liquidity, which is costly </li></ul><ul><ul><li>In US liquidity worth ~3% of daily flows are used for settlement, i.e. daily speed of circulation is ~33. </li></ul></ul>
  3. 3. Papers <ul><li>Soramäki, Kimmo, M.L. Bech, J. Arnold, R.J. Glass and W.E. Beyeler (2007). &quot;The Topology of Interbank Payment Flows&quot;. Physica A. Vol. 379 . </li></ul><ul><li>Models payment flows among banks as graphs (“topology”) </li></ul><ul><li>Beyeler, Walter, M.L Bech, R.J. Glass, and K. Soramäki (2007). &quot;Congestion and Cascades in Payment Systems&quot;. Physica A. Forthcoming. </li></ul><ul><ul><li>Models the coupling of payment flows and flow dynamics (“physics”) </li></ul></ul><ul><li>Galbiati, Marco and Kimmo Soramäki (2007). “Dynamic model of funding in interbank payment systems ”. Bank of England Working Paper. Forthcoming. </li></ul><ul><ul><li>Models bank decision-making (“behavior”) </li></ul></ul>
  4. 4. <ul><li>Payment system is modeled as a graph of liquidity flows ( links ) between banks ( nodes ) </li></ul>“Topology”
  5. 5. Fedwire liquidity flows Fedwire liquidity flows share many of the characteristics commonly found in other empirical complex networks - scale-free (power law) degree distribution - high clustering coefficient - small world phenomenon - short paths (avg 2.6) in spite of low connectivity (0.3%) - structure of networks persistent from day to day - heavily impacted by the terrorist attacks of 9/11, disruption lasted for ~10 days 6600 banks, 70,000 links 66 banks comprise 75% of value 25 banks completely connected
  6. 6. “Physics” <ul><li>Model of the dynamics that take place in payment system under simple rules of settlement </li></ul><ul><li>Interaction of simple local rules –> emergent system level behaviour </li></ul>
  7. 7. Payment System 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 Influence of liquidity 1
  8. 8. 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 Payment System Instructions Payments Liquidity Influence of liquidity 2
  9. 9. Payment System Instructions Payments At very low liquidity payments are controlled by internal dynamics. Settlement cascades are larger and can pass through the same bank numerous times Liquidity Influence of liquidity 3
  10. 10. Payment System Instructions Payments A liquidity market substantially reduces congestion using only a small fraction (e.g. 2%) of payment-driven flow Liquidity Market Influence of a liquidity market
  11. 11. “Behavior” <ul><li>Modeling banks as decision makers where each bank’s best action depends on the actions of other banks. </li></ul>
  12. 12. <ul><li>Banks choose an opening balance at the beginning of each day </li></ul><ul><li>Banks face uncertainty about the opening balances of other banks </li></ul><ul><li>Banks face funding costs and delay costs, which depend on the opening balances (and the random arrival of payment instructions). </li></ul><ul><li>Banks adapt their level of opening balances over time (by means of Fictitious play), depending on observed actions by others </li></ul><ul><li>The game is played until convergence of beliefs takes place </li></ul>Funding behavior model
  13. 13. <ul><li>Costs are minimized at different liquidity levels, depending on liquidity posted by other banks, e.g. (for n=15, delay cost=5) </li></ul><ul><ul><li>if others post 1, I should post 24 </li></ul></ul><ul><ul><li>if others post 5, I should post 15 </li></ul></ul><ul><ul><li>if others post 50, I should post 10 </li></ul></ul>funds committed by i cost, i funds committed by <j> Illustration of costs and best replies
  14. 14. <ul><li>Banks (naturally) use more liquidity when delay price is high </li></ul><ul><li>The amount used increases rapidly as delay price is increased from 0 </li></ul><ul><li>Banks will practically not commit over 49 units </li></ul>for n=15 ; 200 payments of unit size per bank price of delays price of delays funds committed by i funds committed by i Results 1 – base case
  15. 15. <ul><li>Performance of a payment system is a function of topology , physics and behavior – one factor alone is not enough to evaluate efficiency or robustness </li></ul><ul><li>Graph theory provides good tools for analyzing the structure of interbank payment systems and their liquidity flows and e.g. for identifying important banks </li></ul><ul><li>Statistical mechanics help understand the impact of settlement rules on system performance (simple local rules -> emergent system level behavior) </li></ul><ul><li>Depending on topology, physics and cost parameters, different “liquidity games” emerge, and thus different system level behavior </li></ul><ul><li>The complete model developed in conjunction with the presented work is modular (programmed in Java) and can be easily enriched and used to analyze real policy questions (not only interbank payments) </li></ul>Conclusions
  16. 16. example: Fedwire around 9/11 2001 Topology and disruption 1