Download manual (631K PDF) - DefaultRisk.com
Download manual (631K PDF) - DefaultRisk.com Download manual (631K PDF) - DefaultRisk.com
CDO models: Opening the black box – Part four The market’s choice… Copulas have become the workhorse of dependency modelling ► ► ► ► ► ► ► Since its introduction to credit derivatives pricing by Li (1999), copulas have become the market’s choice to model the dependency structure inherent in credit portfolios The reasons for that are manifold and include ease of implementation and calibration The main benefit however is the simple and intuitive way in which even complex dependency structures can be incorporated into a generic model The results are dependent default times, which also can be easily calibrated to individual, market based, credit spreads This is achieved by splitting the description of the specific default behaviour of each credit in the portfolio (the marginal distributions) from their joint behaviour. This means that we can model separately the individual marginals and the correlation structure (or in more general terms their dependency structure) For simplicity, we restrict our model to cases where the marginal and the joint distribution are of the same kind, while with very little extra work, the user can modify this aspect Copula models are powerful tools for pricing and risk management of different correlation products, ranging from FTD’s to synthetic CDO tranches. In all cases the loss distributions are derived from the simulated default times, based on the assumptions used for spreads, recoveries and dependency 3
CDO models: Opening the black box – Part four What is a Copula The maths.. Copulas allow expressing joint probability distributions independent from the shape of their marginals.. ► The joint distribution function C of m uniform random variables U 1 , U 2 , …, U m can be referred to as copula function: ► ► C [ U ≤ u , U ≤ u , ... U ≤ u ] ( u1 , u 2 , ... , u m , ρ ) Pr 1 1 2 2 = , A set of univariate marginal distribution functions can be linked via a copula function, resulting in a multivariate distribution function: ( F ( x ) F ( x ), ... , F ( x )) F ( x , x , ... x ) C , 1 1 , 2 2 m m = 1 2 Sklar’s theorem (1959) established the converse, showing that any multivariate distribution function F can be expressed via a copula function ‣ For any multivariate distribution, the univariate marginal distributions and their dependency structure can be separated ► Let F(x 1 , x 2 , …, x m ) be a joint multivariate distribution function with univariate marginal distribution functions F 1 (x 1 ), F 2 (x 2 ), …,F m (x m ) m m m ► ► ► Then there exists a copula function C(u 1 ,u 2 ,…,u m ) such that: C is unique if each F i is continuous ( x x , ... , x ) C ( F ( x ), F ( x ), ... F ( x )) F , 1 , 2 m = 1 1 2 2 Depending on the type of copula, the dependency structure will be expressed in terms of a correlation matrix, as in case for the normal copula, or via a smaller number of parameters as in case of Archimedean copulas (such as the Clayton or Gumbel copula) m m 4
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CDO models: Opening the black box – Part four<br />
What is a Copula The maths..<br />
Copulas allow expressing joint probability distributions independent from the shape of their marginals..<br />
► The joint distribution function C of m uniform random variables U 1 , U 2 , …, U m can be referred to as copula function:<br />
►<br />
►<br />
C<br />
[ U ≤ u , U ≤ u , ... U ≤ u ]<br />
( u1 , u 2 , ... , u m , ρ ) Pr 1 1 2 2<br />
= ,<br />
A set of univariate marginal distribution functions can be linked via a copula function, resulting in a multivariate distribution function:<br />
( F ( x ) F ( x ),<br />
... , F ( x )) F ( x , x , ... x )<br />
C ,<br />
1 1 , 2 2 m m = 1 2<br />
Sklar’s theorem (1959) established the converse, showing that any multivariate distribution function F can be expressed via a copula<br />
function<br />
‣ For any multivariate distribution, the univariate marginal distributions and their dependency structure can be separated<br />
► Let F(x 1 , x 2 , …, x m ) be a joint multivariate distribution function with univariate marginal distribution functions F 1 (x 1 ), F 2 (x 2 ), …,F m (x m )<br />
m<br />
m<br />
m<br />
►<br />
►<br />
►<br />
Then there exists a copula function C(u 1 ,u 2 ,…,u m ) such that:<br />
C is unique if each F i is continuous<br />
( x x , ... , x ) C ( F ( x ),<br />
F ( x ),<br />
... F ( x ))<br />
F ,<br />
1 , 2 m = 1 1 2 2<br />
Depending on the type of copula, the dependency structure will be expressed in terms of a correlation matrix, as in case for the normal<br />
copula, or via a smaller number of parameters as in case of Archimedean copulas (such as the Clayton or Gumbel copula)<br />
m<br />
m<br />
4
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