WHAT DO WE KNOW ABOUT EXPOSURE AT DEFAULT ON CONTINGENT CREDIT LINES? A SURVEY OF THE LITERATURE, EMPIRICAL ANALYSIS AND MODELS
AbstractExposure at Default (EAD) quantification for the large exposures to contingent credit lines (CCLs) is a
critical for models of credit risk amongst financial institutions. This includes expected loss calculations for loan
provisions, economic credit capital as well as regulatory capital under the Basel II advanced Internal Ratings
Based (IRB) framework. However, banks struggle in quantifying EAD due to limited empirical evidence and
industry benchmarks, unavailable or inconsistent internal data and paucity of practical models. This study
contributes to this modeling effort by surveying the existing literature and consolidating the empirical evidence on
EAD. We consider recent extensions of prior empirical work that considers alternative determinants and
measures of EAD risk in addition to the traditional approaches, including regression models and actuarial based
models of EAD. We illustrate these new EAD paradigms through an empirical investigation using a sample of
Moody’s rated defaulted firms, first the construction of a predictive econometric model in the generalized linear
model class, followed by the calibration of an EAD model similar to basic CreditRisk+ type using Fast Fourrier
transforms to convolute portfolio segments.
The Copyright Transfer Form to ASERS Publishing (The Publisher)
This form refers to the manuscript, which an author(s) was accepted for publication and was signed by all the authors.
The undersigned Author(s) of the above-mentioned Paper here transfer any and all copyright-rights in and to The Paper to The Publisher. The Author(s) warrants that The Paper is based on their original work and that the undersigned has the power and authority to make and execute this assignment. It is the author's responsibility to obtain written permission to quote material that has been previously published in any form. The Publisher recognizes the retained rights noted below and grants to the above authors and employers for whom the work performed royalty-free permission to reuse their materials below. Authors may reuse all or portions of the above Paper in other works, excepting the publication of the paper in the same form. Authors may reproduce or authorize others to reproduce the above Paper for the Author's personal use or for internal company use, provided that the source and The Publisher copyright notice are mentioned, that the copies are not used in any way that implies The Publisher endorsement of a product or service of an employer, and that the copies are not offered for sale as such. Authors are permitted to grant third party requests for reprinting, republishing or other types of reuse. The Authors may make limited distribution of all or portions of the above Paper prior to publication if they inform The Publisher of the nature and extent of such limited distribution prior there to. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in The Paper. This agreement becomes null and void if and only if the above paper is not accepted and published by The Publisher, or is with drawn by the author(s) before acceptance by the Publisher.