Measurement of Credit Risk

Brief description

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

Bluhm, C., Overbeck, L., Wagner, C., 2010, An Introduction to Credit Risk Modeling, 2nd ed., Chapman & Hall/CRC; Glasserman, P., 2003, Monte Carlo Methods in Financial Engineering, Springer; Scandizzo, S., 2016, The Validation of Risk Models: A Handbook for Practitioners, Palgrave Macmillan; Witzany, J., 2017, Credit Risk Management: Pricing, Measurement, and Modeling, Springer

Planned learning activities and teaching methods

Interactive teaching (lecture and discussion), blended learning (online exercises are mandatory), application of models on practical problem sets

Assessment methods and criteria

30% mid-term test, 70% written final examination

Prerequisites and co-requisites

FOEC10, FUFI10, FUMS10, MUME10, PRDA10, TSAN10

Infos

Degree programme

Quantitative Asset and Risk Management (Master)

Cycle

Master

ECTS Credits

4.00

Language of instruction

English

Curriculum

Part-Time

Academic year

2024

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. They are also 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.

Course code

0613-09-01-BB-EN-10