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© Brammertz Consulting, 2009 1Date: 22.04.23
Chapter 5: Counterparty
Willi Brammertz / Ioannis Akkizidis
Unified Financial AnalysisRisk & Finance Lab
© Brammertz Consulting, 2009 2Date: 22.04.23
Input elementsCounterparties
© Brammertz Consulting, 2009 3Date: 22.04.23
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
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Descriptive part
Data driven
Well known facts
© Brammertz Consulting, 2009 5Date: 22.04.23
Descriptive DataCharacteristics
> Name
> Street
> Income
> ....
> Target: PD
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Descriptive DataHierarchies
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Descriptive DataInheritance to financial contracts
Counter-party
Contract 1 Contract 2 Contract n
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Descriptive DataCredit enhancements
> Credit enhancements are financial contracts itself
> However: Special Role
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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
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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
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Exposure
Exposure and valuation!
PD
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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”
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EADCredit enhancements: Overview
> Gross exposure
> Credit enhancements
> Net position := EAD
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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
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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?)
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Credit lines
Undrawn part has high probability of being drawn in case of default
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Credit lines and exposure
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Modeling part
Model driven
Quality difference with data driven part
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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
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Recovery rates
> Net recovery
> Recovery rates
> Recovery patterns
> Gross recovery
> Mingles collateral and recovery
> To be avoided if possible
© Brammertz Consulting, 2009 21Date: 22.04.23
Recovery rates
> Based on historical experience
> Single percentage number
© Brammertz Consulting, 2009 22Date: 22.04.23
Recovery pattern
Recovery patterns
© Brammertz Consulting, 2009 23Date: 22.04.23
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
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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)
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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
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A B C D
A 0.95 0.00 0.00 0.05
B 0.00 0.86 0.00 0.14
C 0.00 0.00 0.76 0.24
D 0.00 0.00 0.00 1.00
Ratings and PD
> Ratings must turn into probability of default
> Different expressions> Scalar
> Vector
> Matrix (migration matrix)
A B C D
A 0.95 0.04 0.01 0.00
B 0.05 0.86 0.07 0.02
C 0.01 0.03 0.76 0.20
D 0.00 0.00 0.00 1.00
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Effects of default
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CDO’s
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CDO’s and rating
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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
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Credit limitsExample of a system
Industry 1(1200)