Unified Financial Analysis
Risk & Finance Lab
Chapter 5: Counterparty
Willi Brammertz / Ioannis Akkizidis
© Brammertz Consulting, 2009
Date: 08.04.2015
1
Input elements
Counterparties
© Brammertz Consulting, 2009
Date: 08.04.2015
2
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, 2009
Date: 08.04.2015
3
Descriptive part
Data driven
Well known facts
© Brammertz Consulting, 2009
Date: 08.04.2015
4
Descriptive Data
Characteristics
>
>
>
>
Name
Street
Income
....
> Target: PD
© Brammertz Consulting, 2009
Date: 08.04.2015
5
Descriptive Data
Hierarchies
© Brammertz Consulting, 2009
Date: 08.04.2015
6
Descriptive Data
Inheritance to financial contracts
Counterparty
Contract 1
© Brammertz Consulting, 2009
Contract 2
Date: 08.04.2015
Contract n
7
Descriptive Data
Credit enhancements
> Credit enhancements are financial contracts itself
> However: Special Role
© Brammertz Consulting, 2009
Date: 08.04.2015
8
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, 2009
Date: 08.04.2015
9
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, 2009
Date: 08.04.2015
10
Exposure
PD
Exposure and valuation!
© Brammertz Consulting, 2009
Date: 08.04.2015
11
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, 2009
Date: 08.04.2015
12
EAD
Credit enhancements: Overview
> Gross exposure
> Credit enhancements
> Net position := EAD
© Brammertz Consulting, 2009
Date: 08.04.2015
13
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, 2009
Date: 08.04.2015
14
Credit enhancements
Guarantees and Credit derivatives
>
>
>
>
>
>
Guarantee as special Contract Type
>
Guarantees ,especially credit derivatives are non-life
insurance products
>
Guarantors should model reserves (AIG?)
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!
© Brammertz Consulting, 2009
Date: 08.04.2015
15
Credit lines
Undrawn part has high probability of being drawn in case
of default
© Brammertz Consulting, 2009
Date: 08.04.2015
16
Credit lines and exposure
© Brammertz Consulting, 2009
Date: 08.04.2015
17
Modeling part
Model driven
Quality difference with data driven part
© Brammertz Consulting, 2009
Date: 08.04.2015
18
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, 2009
Date: 08.04.2015
19
Recovery rates
> Net recovery
> Recovery rates
> Recovery patterns
> Gross recovery
> Mingles collateral
and recovery
> To be avoided if
possible
© Brammertz Consulting, 2009
Date: 08.04.2015
20
Recovery rates
> Based on historical experience
> Single percentage number
© Brammertz Consulting, 2009
Date: 08.04.2015
21
Recovery pattern
Recovery patterns
© Brammertz Consulting, 2009
Date: 08.04.2015
22
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, 2009
Date: 08.04.2015
23
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, 2009
Date: 08.04.2015
24
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, 2009
Date: 08.04.2015
25
Ratings and PD
> Ratings must turn into probability of default
> Different expressions
> Scalar
> Vector
> Matrix (migration matrix)
A
B
C
D
A
0.95
0.00
0.04
0.00
0.01
0.05
0.00
B
0.00
0.05
0.86
0.00
0.07
0.14
0.02
C
0.00
0.01
0.00
0.03
0.76
0.24
0.20
D
0.00
0.00
0.00
1.00
© Brammertz Consulting, 2009
Date: 08.04.2015
26
Effects of default
© Brammertz Consulting, 2009
Date: 08.04.2015
27
CDO’s
© Brammertz Consulting, 2009
Date: 08.04.2015
28
CDO’s and rating
© Brammertz Consulting, 2009
Date: 08.04.2015
29
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, 2009
Date: 08.04.2015
30
Credit limits
Example of a system
Trading
2000
C1
(1000)
Country
Industry
I1
(500)
I2
(700)
C2
(1500)
I3
(400)
I1
(1000)
I3
(700)
Industry 1
(1200)
© Brammertz Consulting, 2009
Date: 08.04.2015
31