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Counterparty scenarios


scenarios = getScenarios(cdc,scenarioIndices)



scenarios = getScenarios(cdc,scenarioIndices) returns counterparty scenario details as a matrix of individual losses for each counterparty for the scenarios requested in scenarioIndices.

The simulate function must be run before getScenarios is used. For more information on using a creditDefaultCopula object, see creditDefaultCopula.


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Load saved portfolio data.

load CreditPortfolioData.mat;

Create a 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: []

Set the VaRLevel to 99%.

cdc.VaRLevel = 0.99;

Use the simulate function before running getScenarios. Use the getSenarios function with the creditDefaultCopula object to generate the scenarios matrix.

cdc = simulate(cdc,1e5);
scenarios = getScenarios(cdc,[2,3]);
% expected loss for each scenario
ans = 1×2

    0.1382    1.1461

Input Arguments

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creditDefaultCopula object obtained after running the simulate function.

For more information on creditDefaultCopula objects, see creditDefaultCopula.

Specifies which scenarios are returned, entered as a vector.

Output Arguments

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Counterparty losses, returned as NumCounterparties-by-N matrix where N is the number of elements in scenarioIndices.


If the number of scenarios requested is large, then the output matrix, scenarios, could be large and potentially limited by the available machine memory.


[1] Crouhy, M., Galai, D., and Mark, R. “A Comparative Analysis of Current Credit Risk Models.” Journal of Banking and Finance. Vol. 24, 2000, pp. 59–117.

[2] Gordy, M. “A Comparative Anatomy of Credit Risk Models.” Journal of Banking and Finance. Vol. 24, 2000, pp. 119–149.

[3] Gupton, G., Finger, C., and Bhatia, M. “CreditMetrics – Technical Document.” J. P. Morgan, New York, 1997.

[4] Jorion, P. Financial Risk Manager Handbook. 6th Edition. Wiley Finance, 2011.

[5] Löffler, G., and Posch, P. Credit Risk Modeling Using Excel and VBA. Wiley Finance, 2007.

[6] McNeil, A., Frey, R., and Embrechts, P. Quantitative Risk Management: Concepts, Techniques, and Tools. Princeton University Press, 2005.

Introduced in R2017a