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Default estimation, correlated defaults, and expert information
Author(s) -
Kiefer Nicholas M.
Publication year - 2011
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1124
Subject(s) - default , computer science , basel ii , capital requirement , inference , econometrics , principle of maximum entropy , probabilistic logic , probability of default , loss given default , portfolio , actuarial science , bayesian probability , economics , credit risk , finance , artificial intelligence , profit (economics) , microeconomics
Abstract Capital allocation decisions are made on the basis of an assessment of creditworthiness. Default is a rare event for most segments of a bank's portfolio and data information can be minimal. Inference about default rates is essential for efficient capital allocation, for risk management and for compliance with the requirements of the Basel II rules on capital standards for banks. Expert information is crucial in inference about defaults. A Bayesian approach is proposed and illustrated using prior distributions assessed from industry experts. A maximum entropy approach is used to represent expert information. The binomial model, most common in applications, is extended to allow correlated defaults yet remain consistent with Basel II. The application shows that probabilistic information can be elicited from experts and econometric methods can be useful even when data information is sparse. Copyright © 2009 John Wiley & Sons, Ltd.