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Chapter 5: Counterparty Willi Brammertz / Ioannis Akkizidis

Unified Financial Analysis Risk & Finance Lab. Chapter 5: Counterparty Willi Brammertz / Ioannis Akkizidis. Input elements Counterparties. Counterparty and Behavior. Counterparty has descriptive and modeling part Descriptive part Characteristics Hierarchies Links to financial contracts

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Chapter 5: Counterparty Willi Brammertz / Ioannis Akkizidis

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  1. Unified Financial Analysis Risk & Finance Lab Chapter 5: Counterparty Willi Brammertz / Ioannis Akkizidis

  2. Input elementsCounterparties

  3. Counterparty and Behavior • Counterparty has descriptive and modeling part • Descriptive part • Characteristics • Hierarchies • Links to financial contracts • Credit enhancements • Behavioral (statistical nature) • Probability of default • Recovery rates • Recovery patterns • Used at default

  4. Descriptive part Data driven Well known facts

  5. Descriptive DataCharacteristics • Name • Street • Income • .... • Target: PD

  6. Descriptive DataHierarchies

  7. Descriptive DataInheritance to financial contracts Counter- party Contract 1 Contract 2 Contract n

  8. Descriptive DataCredit enhancements • Credit enhancements are financial contracts itself • However: Special Role

  9. Three steps to expected loss • Exposure at default EAD: Gross exposure – credit enhancements = EAD • Loss given default LGD:EAD * (1 - recovery rate) = LGD • Expected loss EL:LGD * probability of default = EL • Different data quality in each step: separation necessary • Rating agencies: mix the three steps (subprime) • PD‘s must reflect uncollateralized junior debt

  10. Three steps to expected loss • Exposure at default EAD: Gross exposure – credit enhancements = EAD • Loss given default LGD:EAD * (1 - recovery rate) = LGD • Expected loss EL:LGD * probability of default = EL

  11. Exposure PD Exposure and valuation!

  12. Gross exposure • Description of counterparty: • Unique ID • Private: Age, gender, martial status etc. • Firms: Balance sheet ratios, turnover, profitability , market environment etc. • Hierarchies • Assets outstanding per counterparty • Goss exposure := Sum of all assets per “node”

  13. EADCredit enhancements: Overview • Gross exposure • Credit enhancements • Net position := EAD

  14. Credit enhancementsCollateral and Close out nettings • Financial collateral can be modeled as • Normal financial contracts • With a special role • Physical collateral can be modeled as commodity • Close out nettings is a relationship between asset and liability contracts of the same counterparty

  15. Credit enhancementsGuarantees and Credit derivatives • Guarantee as special Contract Type • Guarantee is underlying of credit derivatives • Rating of guarantor must be higher than obligor • Exposure moves from obligor to guarantor • Credit default swaps are standardized guarantees • Double default! • Guarantees ,especially credit derivatives are non-life insurance products • Guarantors should model reserves (AIG?)

  16. Credit lines Undrawn part has high probability of being drawn in case of default

  17. Credit lines and exposure

  18. Modeling part Model driven Quality difference with data driven part

  19. Three steps to expected loss • Exposure at default EAD: Gross exposure – credit enhancements = EAD • Loss given default LGD:EAD * (1 - recovery rate) = LGD • Expected loss EL:LGD * probability of default = EL

  20. Default sequence Default Recovery Exposure

  21. Recovery rates • Gross recovery • Mingles collateral and recovery • To be avoided if possible • Net recovery • Recovery rates • Recovery patterns

  22. Recovery rates • Based on historical experience • Single percentage number

  23. Recovery pattern Recovery patterns

  24. Three steps to expected loss • Exposure at default EAD: Gross exposure – credit enhancements = EAD • Loss given default LGD:EAD * (1 - recovery rate) = LGD • Expected loss EL:LGD * probability of default = EL

  25. Credit rating • Rating is based on • Characteristics as given by descriptive data • Payment behavior (Scoring) • Internal • External • Ratings can be considered to be external • Rating agencies must become more independent of the rated company (e.g. Dodd-Frank, S&P being sued)

  26. Credit ratingPitfalls • Rating vs. Probability of default • Rating and collateral: • Relationship not really clear • Often mingled • Ideally: Rating on uncollateralized junior debt • In this case: Rating corresponds to PD

  27. Ratings and PD • Ratings must turn into probability of default • Different expressions • Scalar • Vector • Matrix (migration matrix)

  28. Effects of default

  29. CDO’s

  30. CDO’s and rating

  31. Credit limits • Coarse but effective risk control instrument • Limits exposure on • Single counterparty • Industry • Region • Risk factors (FX limit, interest rate exposure...) • Etc. • Higher order limits usually < sum of lower order

  32. Credit limitsExample of a system Industry 1 (1200)

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