How Undisciplined Innovation Caused A Crisis and How Its Recurrence Can Be Prevented, Steve Lindo
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1) Wall Street increasingly embraced sophisticated mathematics and modeling in the 1980s and 1990s as quantitative hedge funds produced large gains and investment banks transitioned to using mathematics as a competitive advantage over traditional judgment. 2) Models were used for options pricing, relative value trading, portfolio theory, default probabilities, loss forecasting, credit default swaps, value-at-risk, capital adequacy, and debt ratings. However, the models made erroneous assumptions that distributions were normal and correlations were stable, failed to capture non-linear outcomes, and did not include scenarios of falling housing prices. 3) To prevent future crises, the author argues for realigning incentives to discourage excessive risk-taking, requiring trials of
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How Undisciplined Innovation Caused A Crisis and How Its Recurrence Can Be Prevented, Steve Lindo
- 1. Math and Modeling on Wall Street How Undisciplined Innovation Caused A Crisis and How Its Recurrence Can Be Prevented Steve Lindo Director of Treasury Management & Mortgage Risk Fifth Third Bancorp © SRL Advisory Services © Steve Lindo - November 2011
- 2. Contents 2 Boston, May 2010 Introduction I. Setting the Stage • When, why and how did Wall Street embrace mathematics? • Why did the wheels come off the train? II. Backlot Tour • Principal uses of mathematics on Wall Street III. Nightmare on Wall Street • The most egregious modeling errors IV. Must There be a Sequel? • Potential measures to curb excesses Conclusion © Steve Lindo - November 2011
- 3. I. Setting the Stage Boston, May 2010 When and why did Wall Street use mathematics? The changing of the guard • Quantitative investing hedge funds began to produce spectacular gains (mid-80’s) • Investment banks changed from private partnerships to public companies: Morgan Stanley (‘86), Lehman Brothers (’94) and Goldman Sachs (‘99) • Bond traders displaced traditional investment bankers as leaders of the Wall Street oligarchy Shadow banking dis-intermediated traditional lending • Mathematics was the key to securitization of consumer loans in mid-80’s, an instant hit with investors • Insatiable MBS demand in the late 90’s triggered explosive growth of unlicensed mortgage brokers and non-bank lenders 3 © Steve Lindo - November 2011
- 4. I. Setting the Stage Boston, May 2010 How did Wall Street use mathematics? Mathematization of investment decisions • Wall Street became a “science park” for investment theory innovations • Institutional investors embraced portfolio theory • Statistical regressions and modeling gained popularity, eclipsing experience-based judgment and qualitative analysis Pursuit of profit, not protection from losses • Mathematics was embraced by investment banks as a competitive edge • Product complexity and profitability were interdependent • Mathematics for risk analysis and mitigation was just an afterthought 4 © Steve Lindo - November 2011
- 5. I. Setting the Stage Boston, May 2010 Why did the wheels come off the train? Perfect storm • Hubris • Leverage • Procyclicality Epidemic of agency conflicts • Mortgage brokers • Mortgage lenders • Rating agencies • Investment banks No clinical trials • Mass deployment of new technology without rigorous testing of breaking point and side effects would be unthinkable in engineering and pharmaceutical sectors 5 © Steve Lindo - November 2011
- 6. II. Backlot Tour Boston, May 2010 Principal uses of mathematics on Wall Street Option trading • Black-Scholes formula published 1973 • Adopted as standard valuation method for both exchange- traded and over-the-counter options Relative value trading • Cash v cash, cash v derivative, derivative v derivative • Regression analysis to determine spread mean and extremes Portfolio theory • Regression analysis to determine correlations between asset classes • Asset allocation to achieve desired level of diversification 6 © Steve Lindo - November 2011
- 7. II. Backlot Tour Boston, May 2010 Principal uses of mathematics on Wall Street Default probabilities • Historical default frequency analyzed to determine mean and extremes for distinct asset classes through economic cycles • Historical delinquency-to-default migration rates analyzed to determine mean and extremes Expected loss forecasting • Probability of Default x Exposure at Default x Loss Given Default • Recovery values through economic cycles analyzed to determine mean and extremes 7 © Steve Lindo - November 2011
- 8. II. Backlot Tour Boston, May 2010 Principal uses of mathematics on Wall Street Credit default swaps • Single debtor or pool of similar loans • Modeled PD and LGD • Priced like an insurance policy – probability of claim x expected severity of loss, but with no retention or deductible • Easy to hedge – difficult to undo Value at Risk (VaR) • Analysis of historical correlations between market price changes • Measures volatility of net long-short positions, typically to 2 standard deviations = 95% confidence interval • Quantifiable at trade, strategy and portfolio levels 8 © Steve Lindo - November 2011
- 9. II. Backlot Tour Boston, May 2010 Principal uses of mathematics on Wall Street Capital adequacy • Typical methodology - Monte Carlo simulation • Loss modeling confidence interval - typically 3+ standard deviations = 99.9% • Credit risk, market risk, operational risk • Economic capital = unexpected loss = modeled loss – expected loss Stressed correlations • Credit risk - PD and LGD correlations within asset classes • Market risk - stressed VaR across trading strategies Diversification benefit • Between credit, market and operational risks • Between different lines of business 9 © Steve Lindo - November 2011
- 10. II. Backlot Tour Boston, May 2010 Principal uses of mathematics on Wall Street Rating agencies • Traditional ratings approach • Default probabilities back-tested via historical default tables of rated issuers through economic cycles • Estimated recovery rates Structured debt ratings • Modeled default probabilities • Estimated recovery rate for each asset class • Debt ratings mapped to non-structured default tables • Different ratings for different tranches 10 © November 2011
- 11. III. Nightmare on Wall Street Boston, May 2010 Most egregious modeling errors Borrowers and investors don’t behave like gas molecules • Distributions are not normal, ie. fat not thin tails • Movement is not always random, ie. clumping occurs • Not subject to the laws of physics, but to irrationality and herd mentality Standard deviations and correlations break down • Standard deviation is an unreliable measure of probability when distributions are not normal • Correlations can change exponentially under stress Models fails to capture non-linear outcomes • Complex structures containing options and triggers produce non-linear outcomes • Stress-testing is essential to non-linear loss forecasting 11 © Steve Lindo - November 2011
- 12. III. Nightmare on Wall Street Boston, May 2010 Most egregious modeling errors Historical time series • Time series’ selected for credit and market risk models did not contain extreme market conditions • No historical data existed for subprime mortgage defaults Relative value modeling • Liquidity premium in historical prices was not separable • Perverse behavior under extreme market conditions: • Exponential widening of liquidity premium • Inverse correlation caused by flight to liquidity Residential property values • RMBS models were calibrated only for price increases not reductions 12 © Steve Lindo - November 2011
- 13. III. Nightmare on Wall Street Boston, May 2010 Most egregious modeling errors Model-based debt ratings • Traditional debt ratings were based on years of cumulative performance data and qualitative analysis of industry, company and debt instruments • No public differentiation between traditional and structured debt ratings (contrast - GM grains, synthetic lubricants) • Only Moody’s modeled expected losses in tranches, Standard & Poors and Fitch modeled only default probability • Models did not incorporate fall in residential property prices • Regulatory blessing and investor complacency resulted in universal, un-critical acceptance of model-based ratings, despite cautionary message implicit in higher yields 13 © Steve Lindo - November 2011
- 14. IV. Must There be a Sequel? Boston, May 2010 Potential measures to curb excesses Reversion to common sense • If it sounds too good to be true, it probably is • It may be legal, but that doesn’t make it right • In complex structures, in order for one party to profit, another party has to lose Realigning incentives • Mandatory portion to be retained by originating institution in debt securitizations and syndications (“skin in the game”) • Originators to be paid bonus in step with maturity of portfolio • Profit-based incentives to be structured to recognize the uncertain valuation of model-priced portfolios • Incentive compensation for risk managers to be structured to promote healthy challenge to business 14 © Steve Lindo - November 2011
- 15. IV. Must There be a Sequel? Boston, May 2010 Potential measures to curb excesses New product safeguards • Trials required before unrestricted distribution • Adopt quality labeling convention for models Effective systemic risk management and monitoring • Office of Financial Research • Industry-level data pooling • Expert analysis of trends and anomalies • Study the known-knowns, the known-unknowns and the unknown-unknowns • Risk diversification is good for market participants but leads to increased systemic risk • A few simple playground rules enforced by expert supervision more likely to succeed 15 © Steve Lindo - November 2011
- 16. IV. Must There be a Sequel? Boston, May 201016 Changes already implemented Stress testing • Data aggregation, vetting of assumptions Model validation • Not just structure but also implementation Changes not yet made • Vetting of model suitability - theoretical v statistical • Model quality ratings • Industry data repository • Central clearing • Transparency © Steve Lindo - November 2011
- 17. Conclusion Boston, May 201017 Financial mathematics still has a critical role to play Apply lessons learned from successes and failures • The Titanic did not put an end to ships built of steel • Securitization when safely implemented greatly enhances capital market efficiency Accountability • Adopt healthy skepticism and professional standards • Qualities needed: ethics, humility and a sheriff’s badge! © Steve Lindo - November 2011
- 18. Math and Modeling on Wall Street How Undisciplined Innovation Caused A Crisis and How Its Recurrence Can Be Prevented Steve Lindo Director of Treasury Management & Mortgage Risk Fifth Third Bancorp © SRL Advisory Services © Steve Lindo - November 2011