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AMAzing Results first lessons from implementations of Basel II RISK 07 Gerhard Stahl, BaFin

Learn key control indicators and factors for operational loss data modeling, quality assurance, severity frequency modeling, and optimizing risk management practices.

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AMAzing Results first lessons from implementations of Basel II RISK 07 Gerhard Stahl, BaFin

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  1. AMAzing Results first lessons from implementations of Basel IIRISK 07Gerhard Stahl, BaFin Sydney December 11, 2006 Seite 1

  2. Database Modelling Data Model Control Environment Factors Key Control Indicators Operational Loss Data • data sources: • internal data • external consortium • external collection of publicly known cases Sydney December 11, 2006 | 15.09.2014 Seite 2

  3. Database Modelling • data quality assurance: • internal data: data collection processes • external consortium: the custodian’s quality assurance • external publicly known: collection bias • identification of relevant loss data: • boundary to credit, market, timing or irrelevant losses • “acceptance” and possibly splitting of losses: assigning external losses to own business lines • key risk indicators / control environment factors: • often less reliable as an input to modelling • nevertheless important risk control Sydney December 11, 2006 | 15.09.2014 Seite 3

  4. Severity Frequency Stochastic Modelling Extreme Value Theory OpVaR • issues: • extrapolation beyond experience (to the 1000-year event) • how to “back-test” • “merging” of internal and external data, bias removal • infinite mean models? Sydney December 11, 2006 | 15.09.2014 Seite 4

  5. facts: • almost all data external • three models: internal, logNormal, Pareto • 1000-year event (regulatory capital) at the edge of the experience (all external data combined!) • 5000-year event (economic capital) is beyond any experience Sydney December 11, 2006 | 15.09.2014 Seite 5

  6. facts: • blue line is model, black dots are internal data • model is not conservative in this BL/ET cell • 1000-year event (regulatory capital) is beyond internal experience: corresponds to 0.6bp in this graph! • again: need to extrapolate from real loss experience to far tail -> “shape factor” Sydney December 11, 2006 | 15.09.2014 Seite 6

  7. facts: • POT method, GPD fit, estimate of the shape factor for various tresholds • one cell of the BL/ET matrix • fit “looks good” • but xi>1 means infinite mean! • Embrechts & Nesl.: • mixing of incomparable data ? Sydney December 11, 2006 | 15.09.2014 Seite 7

  8. Current state of OpRisk modelling at banks ... • ... lags business practice in P/C insurance: • little to none explicit modelling of accumulation alias dependencies • little to none modelling of “exposure” (tiny step would be to replace gross loss modelling by loss ratios) • little to none modelling of “explaining variables” (e.g. US versus non-US business) • risk models are qualitatively ill-prepared to allow optimization of insurance coverage • no modelling of “reserve risk” Sydney December 11, 2006 | 15.09.2014 Seite 8

  9. Operational Risk Management • aggregation • allocation to business lines • qualitative adjustments • self-assessments • own scenarios Operational Risk Reporting, Control and Management Stress Testing and Scenario Analysis Sydney December 11, 2006 | 15.09.2014 Seite 9

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