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Market Risk Measurement Models, Megastars and Myths

NGB/LNMB Seminar 14 Januari 2000. Market Risk Measurement Models, Megastars and Myths. Introduction by Theo Kocken Rabobank. Contents. Market Risk categories Techniques in practice (trading risk): Value at Risk incl. Extreme Value Theory Pitfalls Alternative: Creative Stress Modeling

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Market Risk Measurement Models, Megastars and Myths

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  1. NGB/LNMB Seminar 14 Januari 2000 Market Risk MeasurementModels, Megastars and Myths Introduction by Theo Kocken Rabobank

  2. Contents • Market Risk categories • Techniques in practice (trading risk): • Value at Risk incl. Extreme Value Theory • Pitfalls • Alternative: Creative Stress Modeling • Failures: Models or management? • Financial Sector Optimisation Problem

  3. Market Risk categories • Trading Risk: • Distribution of Market Value; short term horizon • Asset Management: • Distribution of benchmark deviation; medium term • Asset & Liability Management: • Distribution of future Asset value versus Liabilities; long term horizon • Corporate risk management: • Distribution of Net Worth, annual result etc.;

  4. Trading Risk:The Value at Risk approach • Determine distribution of Market Value of a portfolio at a certain future point in time • Define quantile => VAR

  5. Value at Risk Categories • Variance based approaches [Gaussian distributions] & Mixed distributions • Historical Simulation • Extreme Value Theory

  6. Variance based methods & mixed distributions • Static variance [Markowitz] • Heteroskedastic GARCH models • Exponentially Weighted Moving Average => JP Morgan’s Risk Metrics: famous thanks to good marketing • Bernouilli-Normal Jump process (static & GARCH)

  7. Pro’s & Cons Gaussian models Pro’s: • Easy to implement • For option portfolios, you can generate thousands of scenarios with Monte Carlo simulation Con’s: • Market’s don’t have normal distribution =>especially tails of distribution, where real risk resides

  8. Fat tails

  9. Historical Simulation • Just apply historical (relative) movements as scenarios => generate market value distribution • Easy to apply & non-normality incorporated • But often not enough observations in tail to get a reliable tail distribution

  10. Extreme Value Theory • Tail has different distribution from the rest and tail=key so focus on tail distribution • Approaches: Exponential, Gauchy,… • Pareto and Student-t as practical solution to class of fat-tailed distributions • Estimation challenge: • Not too many observations (tail distribution) • Not too few observations: Variance of estimator too high

  11. Stationary timeseries? • Many crashes are due to changes in (economic) “regimes” • Market Rates (e.g. FX rate) don’t necessarily follow logical relation with underlying variables (e.g. inflation difference=>purchasing power parity??) => economics is essentially different from physics

  12. Examples: • (Semi-)Pegged currencies turning into floating rates. Collapse Bretton Woods ‘72, Asia/Brasil ‘98 etc. • Default of Russia ‘98 and escape to safe havens => Spread widening • Flows of money after disclosure, e.g. LTCM ‘98 => systematic risk…????? Autocorrelation in interest rates ‘94 etc.

  13. Creative Stress Modeling Approach among others: • Analysis of underlying developments and relation to market variables • Estimate of “Stress” built into the system • And effect of contagion

  14. Both VAR and Stress modeling used parallel in practice: • VAR for allocation of limits and capital; Risk Adjusted Return; regulator’s capital etc. • Stress Modeling useful for consolidated event analysis to prevent default or unacceptable swings in annual result

  15. Failures: Models or Management • Quantitative Risk Management could have: • warned UBS etc. on LTCM and other hedge funds • helped CSFB, NatWest etc. on option valuation • prevented Metalgeselshaft from unwinding contracts • etc. • But couldn’t have avoided problems with Barings, Sumitomo, Daiwa

  16. Cont.: Failures: Models or Management • These disasters are due to organisational and procedural issues : • Sound risk organisation from top to bottom & real segregation of duties [Corporate Governance] • sound confirmation, reconciliation procedures etc. • practical product knowledge, systems insight, market feeling • Risk Management = products, markets, systems, procedures, people,…., MODELS

  17. The Financial Sector Optimisation Problem • Maximise: Risk Adjusted Return on Capital • Subject to: • VAR, Event Risk & Credit Risk Limits [in theory: Net Worth > Risk] • Market Share, client targets etc • Risk Adjusted Return = return - average [expected] losses due to risk • Capital ideally comprises cushion for market, credit, ops risk

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