Athula Alwis, Senior Vice President, Global Credit, Surety and Political Risk Practice February 12, 2009

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.”

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The Role of Mathematical Models in the Current Financial Crisis – Lessons for the Export Credit and

Political Risk Business

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Agenda

I. IntroductionII. Liquidity CrisisIII. Credit CrisisIV. Mortgage CrisisV. History of Mathematical ModellingVI. The Role of Models in the Current CrisisVII. What can we learn?

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Introduction

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Introduction

Source: Creators Syndicate

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Introduction

Source: Creative Commons

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Introduction

Source: Creative Commons

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Introduction

Source: Wikimedia Commons; “http://en.wikipedia.org/wiki/Image:Subprime_Crisis.jpg”

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Introduction

€ 320 billion

$ 700 billion +

£150 billion

€ 500 billionEurope $2.3 trillion in total

¥ 10 trillion

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Introduction

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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)

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Introduction

Projected Business Failures in 2009

Japan: 17,000UK: 38,000USA: 62,000France: 63,000

Source: Financial Times and Euler Hermes

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Liquidity Crisis

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Liquidity Crisis

When an entity experiences a shortage of cashTo pay for day-to-day business operations (e.g., Payroll)To meet debt obligations on timeTo expand inventory and production

Does not necessarily mean that the business is insolvent

A specific liquidity risk!

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Liquidity Crisis

When businesses in general experience shortages of cashDue to reduced lending by banksDue to tighter lending standards by banksDue to shortage of cash at banks

A liquidity crisis!

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Liquidity Crisis

Comparison to credit crisisA sound business can experience a liquidity crisis by temporary inaccessibility to required financingA credit crisis is based on insolvency of entities• Due to steep decline of previously over-priced assets

(mortgage-backed securities, CDO, etc)

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Credit Crisis

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Credit Crisis

A material reduction in available credit and / or A significant increase in cost of credit

Widening of credit spreadsIncrease in credit default ratesWeak corporate financialsUnstable capital bases

leading to…

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Credit Crisis

Crisis of insolvencyAnticipated decline in value of collateralIncreased perception of riskChange in monetary conditionsLoss of capital at banks

Lack of confidence in financial markets!

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Mortgage Crisis

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Mortgage Crisis

CDO

Equity Tranche

Mezzanine Senior Tranche

Commercial Paper

SPE MBS

BANK

MORTGAGE LENDER

SIV

BORROWER

HIGH RISK INVESTOR

LOW RISK INVESTOR

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Mortgage Crisis

Key DriversHousing marketUnemploymentInterest rates

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Mortgage Crisis

The cost to economyRecessionLack of financing for solvent companies and individuals with good creditOver 2M job losses so far in the US in 2008 (4.5M overall)Over 2.8M unemployed in the UK

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Mortgage Crisis

The cost to financial institutionsLack 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

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Mortgage Crisis

Other concernsMortgage equity loansStudent loansCredit cardsCorporate real estate

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Mortgage Crisis

Exacerbation of the credit cycleMajor corporate failuresHigh unemploymentStagflation (inflation and economic stagnation)Recession

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Mortgage Crisis – Perfect Storm

Liquidity crisisCredit crisisMortgage crisisRecessionIt may not be over!

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History of Mathematical Modelling

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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

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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 ValueA 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

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Brief History of Credit Modeling

Fischer Black Myron Scholes Robert Merton

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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!

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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

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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

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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

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The Role of Models in the Current Crisis

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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

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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

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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

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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

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What Can We Learn?

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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.

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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 addressedMultiple metrics and models should be employed, if possible (VaR, CTE, Volatility, Scenario Testing, …)

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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 vacuumThe simplifying assumptions should be evaluated for validityUse actual original data (a clear advantage for the export credit and political risk industry)

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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 outModel correlation (risk is not randomly distributed; cannot escape it)Consider systemic risk

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What Can We Learn

Mathematical tools cannot precisely model human behavior

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Q & A