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fryeJacobsLGD

Compute conditional loss given default (LGD) using Frye-Jacobs model

Since R2024b

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    Syntax

    ConditionalLGD = fryeJacobsLGD(ConditionalPD,BaselinePD,BaselineLGD,Correlation)

    Description

    ConditionalLGD = fryeJacobsLGD(ConditionalPD,BaselinePD,BaselineLGD,Correlation) returns the conditional loss given default (LGD), where ConditionalPD is the conditional probability of default (PD), BaselinePD is the baseline PD, BaselineLGD is the baseline LGD, and Correlation is a correlation parameter.

    example

    Examples

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    Open Live Script

    Use the fryeJacobsLGD function to compute the conditional LGD with scalar inputs.

    First, specify the input arguments.

    ConditionalPD = 0.05;
BaselinePD = 0.08;
BaselineLGD = 0.40;
Correlation = 0.20;

    Return the conditional LGD and display the result.

    ConditionalLGD = fryeJacobsLGD(ConditionalPD,BaselinePD,BaselineLGD,Correlation);
disp(ConditionalLGD);
        0.3197
    Open Live Script

    Use the fryeJacobsLGD function to compute the conditional LGD with scalar and vector input arguments.

    First, specify the input arguments.

    ConditionalPD = [0.05 0.10 0.15]';
BaselinePD = [0.08 0.09 0.10]';
BaselineLGD = [0.40 0.45 0.50]';
Correlation = 0.20;

    Return the conditional LGD and display the result.

    ConditionalLGD = fryeJacobsLGD(ConditionalPD,BaselinePD,BaselineLGD,Correlation);
disp(ConditionalLGD);
        0.3197
    0.4151
    0.4971

    Input Arguments

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    Conditional PD, specified as a real-valued numeric scalar or numeric vector with values in the range (0, 1). If you specify a scalar and any other input argument is a vector, ConditionalPD undergoes scalar expansion to match the length of the other vector input arguments. If you specify a vector, its length must match the length of all other vector input arguments.

    Baseline PD, specified as a real-valued numeric scalar or numeric vector with values in the range (0, 1). If you specify a scalar and any other input argument is a vector, BaselinePD undergoes scalar expansion to match the length of the other vector input arguments. If you specify a vector, its length must match the length of all other vector input arguments.

    Baseline LGD, specified as a real-valued numeric scalar or numeric vector with values in the range (0, 1]. If you specify a scalar and any other input argument is a vector, BaselineLGD undergoes scalar expansion to match the length of the other vector input arguments. If you specify a vector, its length must match the length of all other vector input arguments.

    Correlation parameter, specified as a real-valued numeric scalar or numeric vector with values in the range [0, 1). If you specify a scalar and any other input argument is a vector, Correlation undergoes scalar expansion to match the length of the other vector input arguments. If you specify a vector, its length must match the length of all other vector input arguments. For information on estimating Correlation, see Calculating Regulatory Capital with the ASRF Model.

    Output Arguments

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    Conditional LGD, returned as a real-valued numeric scalar or numeric vector.

    More About

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    Frye-Jacobs Function

    The fryeJacobsLGD function for the conditional LGD is based on the following equation:

    ConditionalLGD=Φ(Φ−1(ConditionalPD)−Φ−1(BaselinePD)−Φ−1(BaselinePD*BaselineLGD)1−Correlation)/ConditionalPD

    where Φ is the normal cumulative distribution function (CDF) and Φ–1 is the inverse normal CDF.

    References

    [1] Frye, Jon, and Michael Jacobs. “Credit Loss and Systematic Loss given Default.” The Journal of Credit Risk 8, No. 1 (March 2012): 109–40.

    [2] Frye, Jon. “The simple link from default to LGD.” Risk, London (March 2014): 60–65.

    Version History

    Introduced in R2024b

    See Also

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