1. © Brammertz Consulting, 20091Date: 05.11.2015 Chapter 5: Counterparty Willi Brammertz / Ioannis Akkizidis Unified Financial Analysis Risk & Finance Lab

2. © Brammertz Consulting, 20092Date: 05.11.2015 Input elements Counterparties

3. © Brammertz Consulting, 20093Date: 05.11.2015 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. © Brammertz Consulting, 20094Date: 05.11.2015 Descriptive part Data driven Well known facts

5. © Brammertz Consulting, 20095Date: 05.11.2015 Descriptive Data Characteristics > Name > Street > Income >.... > Target: PD

6. © Brammertz Consulting, 20096Date: 05.11.2015 Descriptive Data Hierarchies

7. © Brammertz Consulting, 20097Date: 05.11.2015 Descriptive Data Inheritance to financial contracts Counter- party Contract 1Contract 2Contract n

8. © Brammertz Consulting, 20098Date: 05.11.2015 Descriptive Data Credit enhancements > Credit enhancements are financial contracts itself > However: Special Role

9. © Brammertz Consulting, 20099Date: 05.11.2015 Three steps to expected loss 1. Exposure at default EAD: Gross exposure – credit enhancements = EAD 2. Loss given default LGD: EAD * (1 - recovery rate) = LGD 3. 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. © Brammertz Consulting, 200910Date: 05.11.2015 Three steps to expected loss 1. Exposure at default EAD: Gross exposure – credit enhancements = EAD 2. Loss given default LGD: EAD * (1 - recovery rate) = LGD 3. Expected loss EL: LGD * probability of default = EL

11. © Brammertz Consulting, 200911Date: 05.11.2015 Exposure Exposure and valuation! PD

12. © Brammertz Consulting, 200912Date: 05.11.2015 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. © Brammertz Consulting, 200913Date: 05.11.2015 EAD Credit enhancements: Overview > Gross exposure > Credit enhancements > Net position := EAD

14. © Brammertz Consulting, 200914Date: 05.11.2015 Credit enhancements Collateral 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. © Brammertz Consulting, 200915Date: 05.11.2015 Credit enhancements Guarantees 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. © Brammertz Consulting, 200916Date: 05.11.2015 Credit lines Undrawn part has high probability of being drawn in case of default

17. © Brammertz Consulting, 200917Date: 05.11.2015 Credit lines and exposure

18. © Brammertz Consulting, 200918Date: 05.11.2015 Modeling part Model driven Quality difference with data driven part

19. © Brammertz Consulting, 200919Date: 05.11.2015 Three steps to expected loss 1. Exposure at default EAD: Gross exposure – credit enhancements = EAD 2. Loss given default LGD: EAD * (1 - recovery rate) = LGD 3. Expected loss EL: LGD * probability of default = EL

20. © Brammertz Consulting, 200920Date: 05.11.2015 Recovery rates > Net recovery > Recovery rates > Recovery patterns > Gross recovery > Mingles collateral and recovery > To be avoided if possible

21. © Brammertz Consulting, 200921Date: 05.11.2015 Recovery rates > Based on historical experience > Single percentage number

22. © Brammertz Consulting, 200922Date: 05.11.2015 Recovery pattern Recovery patterns

23. © Brammertz Consulting, 200923Date: 05.11.2015 Three steps to expected loss 1. Exposure at default EAD: Gross exposure – credit enhancements = EAD 2. Loss given default LGD: EAD * (1 - recovery rate) = LGD 3. Expected loss EL: LGD * probability of default = EL

24. © Brammertz Consulting, 200924Date: 05.11.2015 Credit rating > Rating can be based on > Characteristics as given by descriptive data > Payment behavior (Scoring) > Internal > External > Ratings can be > Internal > External > Rating agencies must become more independent of the rated company (e.g. Dodd-Frank, S&P being sued)

25. © Brammertz Consulting, 200925Date: 05.11.2015 Credit rating Pitfalls > 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

26. © Brammertz Consulting, 200926Date: 05.11.2015 ABCD A0.950.00 0.05 B0.000.860.000.14 C0.00 0.760.24 D0.00 1.00 Ratings and PD > Ratings must turn into probability of default > Different expressions > Scalar > Vector > Matrix (migration matrix) ABCD A0.950.040.010.00 B0.050.860.070.02 C0.010.030.760.20 D0.00 1.00

27. © Brammertz Consulting, 200927Date: 05.11.2015 Effects of default

28. © Brammertz Consulting, 200928Date: 05.11.2015 CDO’s

29. © Brammertz Consulting, 200929Date: 05.11.2015 CDO’s and rating

30. © Brammertz Consulting, 200930Date: 05.11.2015 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

31. © Brammertz Consulting, 200931Date: 05.11.2015 Credit limits Example of a system Industry Country Trading 2000 C1 (1000) I1 (500) I2 (700) I3 (400) C2 (1500) I1 (1000) I3 (700) Industry 1 (1200)