Measurement of Credit Risk

Overview on the determinants of the loss distribution of a single loan (or corporate bond): probability of default PD, loss given default LGD, exposure at default EAD (for credit lines); Methods for the PD estimation of single loans: simple estimate (average historical default frequency of portfolios), intensity models, structural models (option price models), logistic regression and scorecards; LGD estimation for single loans; EAD estimation for credit lines; Methods for computing Value at Risk of a credit portfolio; Introduction to the following topics: Estimation of the loss distribution of portfolios that consist of loans, corporate bonds and derivatives; Pricing and estimation of the loss distribution for asset-backed securities ABS and credit derivatives; Models for back testing and stress testing

Mode of delivery

face to face

Type

compulsory

Recommended or required reading and other learning resources/tools

Engelmann, B., Rauhmeier, R. (ed.), 2006, The Basel II Risk Parameters: Estimation, Validation, and Stress Testing, Springer Bluhm, C., Overbeck, L., Wagner, C., 2002, An Introduction to Credit Risk Modeling, Taylor & Francis Ltd.

Planned learning activities and teaching methods

integrated class

Assessment methods and criteria

Students are assessed on the quality of their assignments, their presentations, their participation and the results of the written quizzes.

Prerequisites and co-requisites

Courses 1 - 6

Infos

Degree programme

Quantitative Asset and Risk Management (Master)

Cycle

Master

ECTS Credits

4.00

Language of instruction

English

Curriculum

Part-Time

Academic year

2021

Semester

2 SS

Incoming

Yes

Learning outcome

After the successful completion of the course students are able to master the various different computational approaches to estimate risk determinants for credit risk (probabilities of default, losses given default and exposures at default). They can estimate the loss distribution of credit portfolios which allows them to estimate risk measures such as the Value at Risk or the Unexpected Loss. Also, they are able to test the quality of already implemented risk measurement models (back-testing) and they can conduct stress tests that analyse the impact of scarce extreme events. This detailed knowledge about credit risk measurement is essential for managing credit risk.

Course code

0613-09-01-BB-EN-10