Generate risk contributions for each counterparty in portfolio
returns a table of risk contributions for each counterparty in the portfolio.
Contributions = riskContribution(
Contributions table allocates the full portfolio
risk measures to each counterparty, such that the counterparty risk
contributions sum to the portfolio risks reported by
When creating a
you can set the
'UseParallel' property if you have
Parallel Computing Toolbox™. Once the
'UseParallel' property is
set, parallel processing is used to compute
Determine the Risk Contribution for Each Counterparty for a
Load saved portfolio data.
creditDefaultCopula object with a two-factor model.
cdc = creditDefaultCopula(EAD,PD,LGD,Weights2F,'FactorCorrelation',FactorCorr2F)
cdc = creditDefaultCopula with properties: Portfolio: [100x5 table] FactorCorrelation: [2x2 double] VaRLevel: 0.9500 UseParallel: 0 PortfolioLosses: 
VaRLevel to 99%.
cdc.VaRLevel = 0.99;
simulate function before running
riskContribution. Then use
riskContribution with the
creditDefaultCopula object to generate the risk
cdc = simulate(cdc,1e5); Contributions = riskContribution(cdc); Contributions(1:10,:)
ans=10×5 table ID EL Std VaR CVaR __ __________ __________ _________ __________ 1 0.036031 0.022762 0.083828 0.13625 2 0.068357 0.039295 0.23373 0.24984 3 1.2228 0.60699 2.3184 2.3775 4 0.002877 0.00079014 0.0024248 0.0013137 5 0.12127 0.037144 0.18474 0.24622 6 0.12638 0.078506 0.39779 0.48334 7 0.84284 0.3541 1.6221 1.8183 8 0.00090088 0.00011379 0.0016463 0.00089197 9 0.93117 0.87638 3.3868 3.9936 10 0.26054 0.37918 1.7399 2.3042
Note: Due to simulation noise or numerical error, the
VaR contribution can sometimes be greater than the
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Contributions = riskContribution(cdc,'VaRWindow',0.3)
VaRWindow — Size of the window used to compute VaR contributions
(default) | numeric between
Size of the window used to compute VaR contributions, specified as
the comma-separated pair consisting of
'VaRWindow' and a scalar numeric with a
percent value. Scenarios in the VaR scenario set are used to
calculate the individual counterparty VaR contributions.
The default is
0.05, meaning that all scenarios
with portfolio losses within 5 percent of the VaR are included when
computing counterparty VaR contributions.
Contributions — Risk contributions
Risk contributions, returned as a table containing the following risk contributions for each counterparty:
EL— Expected loss for the particular counterparty over the scenarios
Std— Standard deviation of loss for the particular counterparty over the scenarios
VaR— Value at risk for the particular counterparty over the scenarios
CVaR— Conditional value at risk for the particular counterparty over the scenarios
Contributions table allocates the full
portfolio risk measures to each counterparty, such that the counterparty
risk contributions sum to the portfolio risks reported by
riskContribution function reports the
individual counterparty contributions to the total portfolio risk measures using
four risk measures: expected loss (EL), standard deviation (Std), VaR, and
ELis the expected loss for each counterparty and is the mean of the counterparty's losses across all scenarios.
Stdis the standard deviation for counterparty i:
Stdi is the standard deviation of losses from counterparty i.
Stdρ is the standard deviation of portfolio losses.
ρij is the correlation of the losses between counterparties i and j.
VaRcontribution is the mean of a counterparty’s losses across all scenarios in which the total portfolio loss is within some small neighborhood around the Portfolio VaR. The default of the
0.05meaning that all scenarios in which the total portfolio loss is within 5% of the portfolio VaR are included in VaR neighborhood.
CVaRis the mean of the counterparty’s losses in the set of scenarios in which the total portfolio losses exceed the portfolio VaR.
 Glasserman, P. “Measuring Marginal Risk Contributions in Credit Portfolios.” Journal of Computational Finance. Vol. 9, No. 2, Winter 2005/2006.
 Gupton, G., Finger, C., and Bhatia, M. “CreditMetrics – Technical Document.” J. P. Morgan, New York, 1997.