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Estimation of Loss Given Default for Low Default Portfolios
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The Basel framework allows banks to assess their credit risk by using their own estimates of Loss Given Default (LGD). However, for a Low Default Portfolio (LDP), estimating LGD is difficult due to shortage of default data. This study evaluates different LGD estimation approaches in an LDP setting by using pooled industry data obtained from a subset of the PECDC LGD database. Based on the characteristics of a LDP a Workout LGD approach is suggested. Six estimation techniques, including OLS regression, Ridge regression, two techniques combining logistic regressions with OLS regressions and two tree models, are tested. All tested models give similar error levels when tested against the data but the tree models might produce rather different estimates for specific exposures compared to the other models. Using historical averages yield worse results than the tested models within and out of sample but are not considerably worse out of time.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2014:26
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-145149OAI: diva2:716848
Subject / course
Mathematical Statistics
Educational program
Master of Science - Industrial Engineering and Management
Available from: 2014-05-13 Created: 2014-05-12 Last updated: 2014-05-13Bibliographically approved

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