Too Interconnected to Fail: CDS Network of US Banks

1. Too Interconnected to Fail: CDS Network of US Banks COMISEF Fellows’ Workshop on Numerical Methods and Optimisation in Finance Birkbeck College, London 18–19 June 2009 Talk by: Ali Raisshaghaghi and Mateusz Gatkowski Project team members: Sheri Markose, Simone Giansante, Matuesz Gatkowski and Ali RaisShaghaghi

2. Agent-based Computational Economics • New economic paradigm rather just a tool kit • Lack of modelling tools • Markets as a complex adaptive system • Intelligent agents • Capable of self-referential calculations and contrarian behaviour • Surprises’ or innovation • Network interconnectivity of agent relationships

3. Object Oriented Programming in ACE

4. Model Observer

5. ACE Framework • Object-oriented simulation platform • Using JAVA • JAS as the simulation frame work • Provides multi threading capabilities and visualisation • Network interconnection simulation • Support for XML data

6. Which Framework? There are some available frameworks (SWARM, NetLogo, JUNG, MASON, JAS, JASA,...) Not all of them customizable

7. Testing Methods • Unit testing •  Confidence that individual units of source code are fit for use • In OO programming units would be classes • Valuable in agent based modelling • Testing the Micro behaviour JUnit: Java framework for unit testing

8. Crisis! • World economy is suffering from the greatest economic crisis since the Great Depression in 1930s. • Alan Greenspan said this is “a century credit tsunami”. • Many central banks take “nonstandard policy”

9. Source: Bankruptcydata.com

10. Loss by Crisis • Huge amount of loss happened in “sub prime” financial crisis. IMF estimate the loss $ 1,405 billion at end of September 2008.

11. Subprime Mortgage Loan & RMBS • The crisis started from the crash of subprime mortgage market.

12. Securitization • The loss of subprime mortgage has spread all over the world through multi-securitization. Securitization Re-Securitization

13. Tranche Mechanism

14. CDOs Secondary Market Insurance ABX Tranches Banks Pension Funds . . . Hedge Funds Mortgagees CDO originators Banks Model

15. UML

16. Cash Asset Financial Contagion • Prime MarketSubprime Borrowers • Real estateMortgage (RMBS) Wholesale and interbank money market • Stock Market • Equity Investment • Short-term capital • Long-term capital Equity Valuation Origination Distribution Structuring: InvestmentBanks Ratings Agencies InvestmentBanks Securitization MBS (CDO) tranches: AAA, AA… LAPF Hedge Fund Insurance DepositsBanks Securities SPV Investment

17. Legend: 0 - 8,6% 8,5% - 10,9% 10,9% - 13,3% 13,3% - 15,7% 15,7% - 18,1% 18,1% - 20,6% Above 20,6% Subprime default rates in US states 2005 2006 February 2008 Source: Based on New York Fed and Wall Street Journal Research data

18. Model Default rate δ(t) = %HLTV&LowFico(t) * %ResetARM(t) +abs(min[0,ΔHousePrice(t)])+max[% Δ i,0], Where: %HLTV&LowFico(t) – a percentage of loans with high Loan-to-Value and Low FICO rating (<620) %ResetARM(t) – a percentage of ARM loans with interest rate reset % Δ i – percentage change in interest rates House prices Δ HousePrice(t) = HP(t)-HP(t-1) = - (a* δ(t-1)+b*[% Δ i(t-1)) . . . States (Mortgagees)

19. Results of simulation vs ABX quotes

20. Credit Default Swap (CDS) Structure Reference Entity A (Bond Issuer) or CDOs A “LENDS” to Reference Entity Default Protection Seller, C “INSURER” (AIG) Default Protection from CDS Buyer, B Premium in bps Payment in case of Default of X = 100 (1-R) Now 3rd party D receives insurance when A defaults; B still owns A’s Bonds ! Party D has incentive to short A’s stocks to trigger failure :Bear Raid B sells CDS to D

21. 20 Banks With CDS Positions($bn) Note: FDIC Data; All figures in $bn

22. Percentage share in CDS market CDS - sell CDS - buy Note: FDIC Data; 4Q 2008

23. Simulation • Financial Contagion

24. Large-Scale ACE • Large interconnection network • Data feeds from financial system • High processing power • Challenges • Investigate the computational challenges and how they can be overcome. • Investigate what is necessary to make a simulation system realistic enough to be • Useful for such a scenario, and how difficult this is. • Analyse the requirements for implementation of a large-agent based systems.

25. Further Developments • Large-Scale Agent based Simulation • Clustered simulation platform • Large data feeds Internet Data Engine Clustered Multi-Agent Simulator

26. Early Warning System !