The Role Of Mathematical Models In The Current Financial Crisis Athula Alwis


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The Role Of Mathematical Models In The Current Financial Crisis Athula Alwis

  1. 1. Athula Alwis, Senior Vice President, Global Credit, Surety and Political Risk Practice February 12, 2009
  2. 2. Robert Merton “At times we can lose sight of the ultimate purpose of the models when their mathematics become too interesting. The mathematics of financial models can be applied precisely, but the models are not all precise in their application to the complex real world. Their accuracy as a useful approximation to that world varies significantly across time and place. The models should be applied in practice only tentatively, with careful assessment of their limitations in each application.”
  3. 3. The Role of Mathematical Models in the Current Financial Crisis – Lessons for the Export Credit and Political Risk Business 3
  4. 4. Agenda I. Introduction II. Liquidity Crisis III. Credit Crisis IV. Mortgage Crisis V. History of Mathematical Modelling VI. The Role of Models in the Current Crisis VII. What can we learn? 4
  5. 5. Introduction 5
  6. 6. Introduction 6 Source: Creators Syndicate
  7. 7. Introduction 7 Source: Creative Commons
  8. 8. Introduction Source: Creative Commons 8
  9. 9. Introduction Source: Wikimedia Commons; “” 9
  10. 10. Introduction ¥ 10 trillion £150 billion € 320 billion Europe $2.3 trillion in total € 500 billion $ 700 billion + 10
  11. 11. Introduction 11
  12. 12. Introduction Unemployment Rates Japan: 3.9% (Dec 2008) UK: 6.0% (Dec 2008) USA: 7.2% (Jan 2009; projected to exceed 10.0%) Germany: 7.6% (Jan 2009) France: 7.9% (Dec 2008) 12
  13. 13. Introduction Projected Business Failures in 2009 Japan: 17,000 UK: 38,000 USA: 62,000 France: 63,000 Source: Financial Times and Euler Hermes 13
  14. 14. Liquidity Crisis 14
  15. 15. Liquidity Crisis When an entity experiences a shortage of cash To pay for day-to-day business operations (e.g., Payroll) To meet debt obligations on time To expand inventory and production Does not necessarily mean that the business is insolvent A specific liquidity risk! 15
  16. 16. Liquidity Crisis When businesses in general experience shortages of cash Due to reduced lending by banks Due to tighter lending standards by banks Due to shortage of cash at banks A liquidity crisis! 16
  17. 17. Liquidity Crisis Comparison to credit crisis A sound business can experience a liquidity crisis by temporary inaccessibility to required financing A credit crisis is based on insolvency of entities • Due to steep decline of previously over-priced assets (mortgage-backed securities, CDO, etc) 17
  18. 18. Credit Crisis 18
  19. 19. Credit Crisis Widening of credit spreads Increase in credit default rates Weak corporate financials Unstable capital bases leading to… A material reduction in available credit and / or A significant increase in cost of credit 19
  20. 20. Credit Crisis Crisis of insolvency Anticipated decline in value of collateral Increased perception of risk Change in monetary conditions Loss of capital at banks Lack of confidence in financial markets! 20
  21. 21. Mortgage Crisis 21
  22. 22. Mortgage Crisis CDO MORTGAGE BORROWER LENDER Mezzanine Senior Tranche LOW RISK HIGH RISK INVESTOR INVESTOR BANK Equity Commercial Tranche Paper SPE MBS SIV 22
  23. 23. Mortgage Crisis Key Drivers Housing market Unemployment Interest rates 23
  24. 24. Mortgage Crisis The cost to economy Recession Lack of financing for solvent companies and individuals with good credit Over 2M job losses so far in the US in 2008 (4.5M overall) Over 2.8M unemployed in the UK 24
  25. 25. Mortgage Crisis The cost to financial institutions Lack of confidence • Bear Stearns and Merrill Lynch acquired • Lehman Brothers – Chapter 11 • Washington Mutual acquired • Goldman Sachs and J P Morgan became banks to survive • Concerns at Citibank and AIG • Issues at RBS Lack of capital for growth 25
  26. 26. Mortgage Crisis Other concerns Mortgage equity loans Student loans Credit cards Corporate real estate 26
  27. 27. Mortgage Crisis Exacerbation of the credit cycle Major corporate failures High unemployment Stagflation (inflation and economic stagnation) Recession 27
  28. 28. Mortgage Crisis – Perfect Storm Liquidity crisis Credit crisis Mortgage crisis Recession It may not be over! 28
  29. 29. History of Mathematical Modelling 29
  30. 30. Brief History of Credit Modeling Ancient Romans traded options against outgoing cargo from seaports Charles Castelli (1877): Book titled “The Theory of Options in Stocks and Shares” Louis Bachelier (1900): Earliest known analytical valuation for options in his mathematics dissertation at Sorbonne Paul Samuelson (1955): Brownian Motion in the Stock Market Resource: A Study of Option Pricing Models, Kevin Rubash, Foster College of Business Administration, Bradley University 30
  31. 31. Brief History of Credit Modeling Richard Kruizenga (1955): Put and Call Options: A Theoretical and Market Analysis James Boness (1962): A Theory and Measurement of Stock Option Value A clear theoretical improvement from previous work and a precursor to … Black Scholes (1973): Option pricing Model Resource: A Study of Option Pricing Models, Kevin Rubash, Foster College of Business Administration, Bradley University 31
  32. 32. Brief History of Credit Modeling Fischer Black Myron Scholes Robert Merton 32
  33. 33. Brief History of Credit Modeling Robert Merton (1973): Relaxed the assumption of no dividends Jonathan Ingerson (1976): Relaxed the assumption of no taxes or transaction costs Robert Merton (1976): Relaxed the restriction of constant interest rates This is the beginning of structural modeling! 33
  34. 34. Brief History of Credit Modeling Vasicek – Kealhofer (1989): Modified Structural model Jarrow – Turnbull (1995): Reduced Form model Duffie – Singleton (1999): Improved Reduced Form model David Li (2001): Incorporated a Gaussian Copula to tackle correlation 34
  35. 35. History of Mathematical Modeling Benefits of Modeling To be disciplined in risk selection and management To be strategic in managing and growing the business To compare against other businesses in terms of risk and rewards To measure and manage risk in a consistent manner 35
  36. 36. History of Mathematical Modeling Benefits of Modeling To question and investigate assumptions, gut instincts and “what if” scenarios To assist in increasing shareholder value To protect the franchise 36
  37. 37. The Role of Models in the Current Crisis 37
  38. 38. The Role of Models in the Current Crisis A heavy reliance on mathematical models by banks, investors and rating agencies The use of inappropriate models to represent complex market conditions Over reliance on unrealistic models 38
  39. 39. The Role of Models in the Current Crisis Use of incorrect ratings from rating agencies Improper calibration of models (lack of reliable data, wrong assumptions, parameter error) The mechanical use of models without properly understanding underlying data, assumptions and economic implications 39
  40. 40. The Role of Models in the Current Crisis Use of single metric to make decisions (For ex. Using VaR to measure one boundary of risk) Lack of awareness of boundaries/break points (for ex. real estate values are bounded by income) The limitations of models were not readily evident Provided false confidence that encouraged additional risk taking by practitioners 40
  41. 41. The Role of Models in the Current Crisis Lack of real world business experience by model users/builders Supported decision making solely based on past patterns Models failed to capture liquidity risk, concentration risk, correlation risk Lack of appreciation for systemic risk and interconnectedness of financial markets at moments of extreme stress 41
  42. 42. What Can We Learn? 42
  43. 43. What Can We Learn A mathematical model is a tool. It cannot and should not replace the practitioner's experience, judgment and business intuition. The major strategic decisions should be guided by business knowledge and common sense of experienced business leaders not by models. 43
  44. 44. What Can We Learn A model must reflect business realities as closely as possible. Using inappropriate models mechanically without exploring the applicability has been a serious issue that must be addressed Multiple metrics and models should be employed, if possible (VaR, CTE, Volatility, Scenario Testing, …) 44
  45. 45. What Can We Learn The assumptions used in any model should be validated by business practitioners. It is imperative that analysts and modelers understand the market conditions, coverage and business processes rather than independently selecting assumptions for models in a vacuum The simplifying assumptions should be evaluated for validity Use actual original data (a clear advantage for the export credit and political risk industry) 45
  46. 46. What Can We Learn The data that go into models should be validated, scrubbed and compared to at least one other independent source. Regular review/upgrade of models and underlying technologies has to be carried out Model correlation (risk is not randomly distributed; cannot escape it) Consider systemic risk 46
  47. 47. What Can We Learn Mathematical tools cannot precisely model human behavior 47
  48. 48. • In preparing this presentation, W illis Re has relied upon data provided by external data sources. N o attem pt has been m ade to independently verify the accuracy of this data. W illis R e does not represent or otherw ise guarantee the accuracy or com pleteness of such data, nor assum e responsibility for the result of any error or om ission in the data or other m aterials gathered from any source in the preparation of this Presentation. W illis R e shall have no liability in connection w ith results stem m ing from the analysis including but not lim ited to any errors, om issions, inaccuracies, or inadequacies associated w ith the data. W illis R e expressly disclaim s any and all liability to any third party in connection w ith this presentation. • In preparing this presentation, W illis Re has used procedures and assum ptions that W illis R e believes are reasonable and appropriate. H ow ever, there are m any uncertainties inherent in actuarial analyses. T hese include, but are not lim ited to, issues such as lim itations in the available data, reliance on client data and outside data sources, the underlying volatility of loss and other random processes, uncertainties that characterize the application of professional judgm ent in estim ates and assum ptions, reinsurance collectability, etc. U ltim ate losses, liabilities and claim s depend upon future contingent events, including, but not lim ited to, unanticipated changes in inflation, law s, and regulations. A s a result of these uncertainties, the actual outcom es could vary significantly from W illis Re’s estim ates in either direction. W illis R e m akes no representation about and does not guarantee the outcom e, results, success, or profitability of any insurance or reinsurance program or venture, w hether or not such program or venture applies the analysis or conclusions contained herein. Please consult your ow n independent professional advisors before m aking any decisions related to any inform ation contained herein. • T his presentation is provided for inform ational purposes only; it is not intended to be relied upon, and is not intended to be a com plete actuarial com m unication. A com plete com m unication can be provided upon request. W illis Re actuaries are available to answ er questions about this presentation. • T he statem ents and opinions included in this presentation are those of the individual speakers and do not necessarily represent the view s of W illis R e or its m anagem ent. Disclaimer 48
  49. 49. Q&A 49