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

© Brammertz Consulting, 20092Date: Input elements Counterparties

© Brammertz Consulting, 20093Date: 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

© Brammertz Consulting, 20094Date: Descriptive part Data driven Well known facts

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

© Brammertz Consulting, 20096Date: Descriptive Data Hierarchies

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

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

© Brammertz Consulting, 20099Date: 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

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: Exposure Exposure and valuation! PD

© Brammertz Consulting, Date: 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”

© Brammertz Consulting, Date: EAD Credit enhancements: Overview > Gross exposure > Credit enhancements > Net position := EAD

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: 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?)

© Brammertz Consulting, Date: Credit lines Undrawn part has high probability of being drawn in case of default

© Brammertz Consulting, Date: Credit lines and exposure

© Brammertz Consulting, Date: Modeling part Model driven Quality difference with data driven part

© Brammertz Consulting, Date: 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

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

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

© Brammertz Consulting, Date: Recovery pattern Recovery patterns

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: 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)

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: ABCD A B C D Ratings and PD > Ratings must turn into probability of default > Different expressions > Scalar > Vector > Matrix (migration matrix) ABCD A B C D

© Brammertz Consulting, Date: Effects of default

© Brammertz Consulting, Date: CDO’s

© Brammertz Consulting, Date: CDO’s and rating

© Brammertz Consulting, Date: 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

© Brammertz Consulting, Date: 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)