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| GaussianLHPLossModel (const Handle< Quote > &correlQuote, const std::vector< Handle< QuantLib::RecoveryRateQuote >> "es) |
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| GaussianLHPLossModel (Real correlation, const std::vector< Real > &recoveries) |
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| GaussianLHPLossModel (const Handle< Quote > &correlQuote, const std::vector< Real > &recoveries) |
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void | update () override |
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Real | expectedTrancheLoss (const Date &d, Real recoveryRate=Null< Real >()) const override |
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Real | probOverLoss (const Date &d, Real remainingLossFraction) const override |
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Real | expectedShortfall (const Date &d, Probability perctl) const override |
| Returns the ESF as an absolute amount (rather than a fraction)
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Real | percentile (const Date &d, Real perctl) const override |
| Value at Risk given a default loss percentile.
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Probability | averageProb (const Date &d) const |
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Real | averageRecovery (const Date &d) const |
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Real | percentilePortfolioLossFraction (const Date &d, Real perctl) const |
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Real | expectedRecovery (const Date &d, Size iName, const DefaultProbKey &ik) const override |
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virtual Real | expectedShortfall (const Date &d, Real percentile) const |
| Expected shortfall given a default loss percentile.
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virtual std::vector< Real > | splitVaRLevel (const Date &d, Real loss) const |
| Associated VaR fraction to each counterparty.
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virtual std::vector< Real > | splitESFLevel (const Date &d, Real loss) const |
| Associated ESF fraction to each counterparty.
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virtual std::map< Real, Probability > | lossDistribution (const Date &) const |
| Full loss distribution.
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virtual Real | densityTrancheLoss (const Date &d, Real lossFraction) const |
| Probability density of a given loss fraction of the basket notional.
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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.
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virtual Probability | probAtLeastNEvents (Size n, const Date &d) const |
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virtual QuantLib::Real | correlation () const |
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Portfolio loss model with analytical expected tranche loss for a large homogeneous pool with Gaussian one-factor copula. See for example "The Normal Inverse Gaussian Distribution for Synthetic CDO pricing.", Anna Kalemanova, Bernd Schmid, Ralf Werner, Journal of Derivatives, Vol. 14, No. 3, (Spring 2007), pp. 80-93. http://www.defaultrisk.com/pp_crdrv_91.htm
It can be used to price a credit derivative or to provide risk metrics of a portfolio.