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Non- and Semi-parametric Methods for Modelling Recovery Rates

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thesis
posted on 13.03.2019, 02:10 by NITHI SOPITPONGSTORN
Following the recent global financial crisis largely caused by loan defaults, the regulator Basel requests banks to hold high capital against credit risk exposure. The estimation of credit risk involves modelling defaulted loan recoveries, which has been found to be challenging in the literature. This thesis develops innovative nonparametric and semiparametric econometric models for loan recoveries, and demonstrates via simulation as well as empirical studies how the key determinants of the recoveries can be found. The outcomes can help banks to design treatment rules for individual borrowers, predict future recoveries of defaulted loans and estimate the accurate level of capital requirement.

History

Campus location

Australia

Principal supervisor

Paramsothy Silvapulle

Additional supervisor 1

Jiti Gao

Year of Award

2018

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Business and Economics