Generalized beta regression models for random loss-given-default | Zendy

Xinzheng Huang | Zendy; Cornelis W. Oosterlee | Zendy

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Generalized beta regression models for random loss-given-default

Author(s) -

Xinzheng Huang,

Cornelis W. Oosterlee

Publication year - 2011

Publication title -

the journal of credit risk

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.259

H-Index - 10

eISSN - 1755-9723

pISSN - 1744-6619

DOI - 10.21314/jcr.2011.150

Subject(s) - expected shortfall , portfolio , econometrics , heteroscedasticity , credit risk , context (archaeology) , skewness , inference , value at risk , computer science , mathematics , economics , actuarial science , risk management , financial economics , paleontology , management , artificial intelligence , biology

We propose a new framework for modeling systematic risk in LossGiven-Default (LGD) in the context of credit portfolio losses. The class of models is very flexible and accommodates well skewness and heteroscedastic errors. The quantities in the models have simple economic interpretation. Inference of models in this framework can be unified. Moreover, it allows efficient numerical procedures, such as the normal approximation and the saddlepoint approximation, to calculate the portfolio loss distribution, Value at Risk (VaR) and Expected Shortfall (ES).

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