Tesis
Modelos preditivos para LGD
Date
2018-05-04Author
Silva, João Flávio Andrade
Institutions
Abstract
Financial institutions willing to use the advanced Internal Ratings Based (IRB) need to develop
methods to estimate the LGD (Loss Given Default) risk component. Proposals for PD (Probability
of default) modeling have been presented since the 1950s, in contrast, LGD’s forecast has
received more attention only after the publication of the Basel II Accord. LGD also has a
small literature, compared to PD, and there is no efficient method in terms of accuracy and
interpretation such as logistic regression for PD. Regression models for LGD play a key role
in the risk management of financial institutions, due to their importance this work proposes a
methodology to quantify the LGD risk component. Considering the characteristics reported
on the distribution of LGD and in the flexible form that the beta distribution may assume, we
propose a methodology for estimation of LGD using the zero inflated bimodal beta regression
model. We developed the zero inflated bimodal beta distribution, presented some properties,
including moments, defined estimators via maximum likelihood and constructed the regression
model for this probabilistic model, presented asymptotic confidence intervals and hypothesis
test for this model, as well as selection criteria of models, we performed a simulation study
to evaluate the performance of the maximum likelihood estimators for the parameters of the
zero inflated bimodal beta distribution. For comparison with our proposal we selected the beta
regression models and inflated beta regression, which are more usual approaches, and the SVR
algorithm, due to the significant superiority reported in other studies.