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Modelling exposure at default without using conversion factors

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posted on 28.03.2022, 17:13 by Mark Thackham
Banks accredited by their regulator to use the Advanced Internal Ratings Based (A-IRB) approach are required to provide their own estimates for calculating their minimum credit capital; these estimates rely on statistical and analytical models to predict Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD). This thesis focusses on estimating EAD for banks granting revolving loans to large corporates and leverages the Global Credit Data (GCD) database. This thesis briefly discusses why risk management, particularly credit risk management, is important for banks and we survey the existing EAD modelling literature which to date has had less focus than PD and LGD modelling. Our prosed methodology models both loan balance at default (EAD) and changes in loan limit at default as random variables, modelling their joint dynamics via a two stage model– the first stage estimates the probability that limits decrease while the second stage estimates EAD conditional on changing limits. To the best of our knowledge, our approach is the first to estimate EAD and changes in loan limit directly for large corporate revolving facilities using the GCD database. Our model suggests that the key drivers of EAD include: limit; balance; utilisation; risk rating; and time to maturity. We also find evidence that banks actively manage limits in the lead up to default, and that these changes in limits have substantial effects on the outcomes of realised EAD.


Table of Contents

1. Executive summary -- 2. Background to credit risk -- 3. Literature review -- 4. Statistical modelling -- Appendices -- Bibliography.


Bibliography: pages 75-77 "November 10, 2015" --title page

Awarding Institution

Macquarie University

Degree Type

Thesis masters coursework


Thesis (MA), Macquarie University, Faculty of Science and Engineering, Department of Statistics

Department, Centre or School

Department of Statistics

Year of Award


Principal Supervisor

Jun Ma


Copyright Mark Thackham 2016. Copyright disclaimer: http://mq.edu.au/library/copyright




1 online resource (1 online resource (77 pages))

Former Identifiers

mq:61587 http://hdl.handle.net/1959.14/1195692