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

© Brammertz Consulting, 2009 1Date: 21.04.23

Chapter 5: Counterparty

Willi Brammertz / Ioannis Akkizidis

Unified Financial AnalysisRisk & Finance Lab


Input elementsCounterparties


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


Descriptive part

Data driven

Well known facts


Descriptive DataCharacteristics

> Name

> Street

> Income

> ....

> Target: PD


Descriptive DataHierarchies


Descriptive DataInheritance to financial contracts

Counter-party

Contract 1 Contract 2 Contract n


Descriptive DataCredit enhancements

> Credit enhancements are financial contracts itself

> However: Special Role


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







Exposure

Exposure and valuation!

PD


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”


EADCredit enhancements: Overview

> Gross exposure

> Credit enhancements

> Net position := EAD


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


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?)


Credit lines

Undrawn part has high probability of being drawn in case of default


Credit lines and exposure


Modeling part

Model driven

Quality difference with data driven part







Recovery rates

> Net recovery

> Recovery rates

> Recovery patterns

> Gross recovery

> Mingles collateral and recovery

> To be avoided if possible


Recovery rates

> Based on historical experience

> Single percentage number


Recovery pattern

Recovery patterns







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)


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


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


Effects of default


CDO’s


CDO’s and rating


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


Credit limitsExample of a system

Industry 1(1200)

Documents

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