Using a Two-stage Model to Estimate the Loss Given Default Distribution
碩士 === 國立東華大學 === 應用數學系 === 105 === We propose a new two-stage model to estimate the loss given default (LGD) distribution. The first-stage model is the logistic regression model used to estimate probabilities of LGD equal to 0 and larger 0, respectively. The second-stage model is the right-tail ext...
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| Format: | Others |
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2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/77591969479318458784 |
| Summary: | 碩士 === 國立東華大學 === 應用數學系 === 105 === We propose a new two-stage model to estimate the loss given default (LGD)
distribution. The first-stage model is the logistic regression model used to estimate
probabilities of LGD equal to 0 and larger 0, respectively. The second-stage model is
the right-tail extended beta model applied to generate the distribution of LGD between
(0,1) and probability of LGD equal to 1. To implement the newly proposed two-stage
model, we collect a sample of 4962 defaulted debts from Moody’s Default and
Recovery Database. The empirical results show that the newly proposed two-stage
model can generate the accurate LGD distribution estimate. Thus, it is useful for
studying the LGD distribution.
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