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void | setupModels () const |
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void | setupModels () const |
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void | setupModels () const |
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void | setupModels () const |
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void | setupModels () const |
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| DefaultLossModel ()=default |
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virtual Probability | probOverLoss (const Date &d, Real lossFraction) const |
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virtual Real | percentile (const Date &d, Real percentile) const |
| Value at Risk given a default loss percentile. More...
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virtual Real | expectedShortfall (const Date &d, Real percentile) const |
| Expected shortfall given a default loss percentile. More...
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virtual std::vector< Real > | splitVaRLevel (const Date &d, Real loss) const |
| Associated VaR fraction to each counterparty. More...
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virtual std::vector< Real > | splitESFLevel (const Date &d, Real loss) const |
| Associated ESF fraction to each counterparty. More...
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virtual std::map< Real, Probability > | lossDistribution (const Date &) const |
| Full loss distribution. More...
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virtual Real | densityTrancheLoss (const Date &d, Real lossFraction) const |
| Probability density of a given loss fraction of the basket notional. More...
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virtual std::vector< Probability > | probsBeingNthEvent (Size n, const Date &d) const |
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virtual Real | defaultCorrelation (const Date &d, Size iName, Size jName) const |
| Pearsons' default probability correlation. More...
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virtual Probability | probAtLeastNEvents (Size n, const Date &d) const |
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virtual Real | expectedRecovery (const Date &, Size iName, const DefaultProbKey &) const |
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template<class BaseModel_T, class Corr2DInt_T>
class QuantLib::BaseCorrelationLossModel< BaseModel_T, Corr2DInt_T >
Base Correlation loss model; interpolation is performed by portfolio (live) amount percentage.
- Though the literature on this model is inmense, see for a more than introductory level (precrisis) chapters 19, 20 and 21 of Modelling single name and multi-name credit derivatives. Dominic O'Kane, Wiley Finance, 2008
- For freely available documentation see:
- Credit Correlation: A Guide; JP Morgan Credit Derivatives Strategy; 12 March 2004
- Introducing Base Correlations; JP Morgan Credit Derivatives Strategy; 22 March 2004
- A Relative Value Framework for Credit Correlation; JP Morgan Credit Derivatives Strategy; 27 April 2004
- Valuing and Hedging Synthetic CDO Tranches Using Base Correlations; Bear Stearns; May 17, 2004
- Correlation Primer; Nomura Fixed Income Research, August 6, 2004
- Base Correlation Explained; Lehman Brothers Fixed Income Quantitative Credit Research; 15 November 2004
- 'Pricing CDOs with a smile' in Societe Generale Credit Research; February 2005
- For bespoke base correlation see:
- Base Correlation Mapping in Lehman Brothers' Quantitative Credit Research Quarterly; Volume 2007-Q1
- You can explore typical postcrisis data by perusing some of the JPMorgan Global Correlation Daily Analytics
- Here the crisis model problems of ability to price stressed portfolios or tranches over the maximum loss are the responsibility of the base models. Users should select their models according to this; choosing the copula or a random loss given default base model (or more exotic ones).
- Notice this is different to a bespoke base correlation loss (bespoke here refering to basket composition, not just attachment levels) ; where loss interpolation is on the expected loss value to match the two baskets. Therefore the correlation surface should refer to the same basket intended to be priced. But this is left to the user and is not implemented in the correlation surface (yet...)
BaseModel_T must have a constructor with a single quote value
Definition at line 92 of file basecorrelationlossmodel.hpp.