MPE_APRE Applied predictive modelling in credit risk

Faculty of Economics and Administration
Autumn 2024
Extent and Intensity
0/0/0. 3 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
Jan Nusko (lecturer), doc. Ing. Daniel Němec, Ph.D. (deputy)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 40 student(s).
Current registration and enrolment status: enrolled: 0/40, only registered: 0/40, only registered with preference (fields directly associated with the programme): 0/40
fields of study / plans the course is directly associated with
Course objectives
Graduates of the course will gain practical experience in managing key risks (credit, market, insurance and regulatory) in banks and insurance companies. The course will introduce the main methods and regulatory constraints that provide the framework for the econometric techniques used to manage these risks. The course will primarily focus on the use of econometrics in the practice of financial institutions, including practical approaches to solving common obstacles such as errors in input data, insufficient number of observations of some modelled states, limited predictive power and violations of assumptions of the derived models, and limits in promoting econometric models into common use in the internal processes of financial institutions.
Learning outcomes
Upon successful completion of the course, students will be able to: - Understand the nature of financial risk management - Assess and quantify operational, market, liquidity and credit risks - Work with basic risk measurement techniques - Understand the specifics of the approval and enforcement process - Gain insight into the use of machine learning and artificial intelligence in risk management
Syllabus
  • Course content: 1. Introduction to risk management, classification and objectives of risk management 2. Credit risk - the biggest scourge of Czech banks - definition, classification, use of statistical methods in measurement (probability of default, scoring and rating, expected and unexpected loss) 3. Approval process (underwriting and scoring) 4. Enforcement process and loss-given default 5. Data sources for scoring 6. Scoring - development of a predictive model for the probability of default 7. Testing and calibration of predictive models
Literature
    required literature
  • HASTIE, Trevor, Robert TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning : data mining, inference, and prediction. 2nd ed. New York, N.Y.: Springer, 2009, xxii, 745. ISBN 9780387848570. info
  • WILMOTT, Paul. Paul Wilmott on quantitative finance. 2nd ed. Chichester: John Wiley & Sons, 2006, xxv, 363. ISBN 0470018704. info
Teaching methods
Method of study, teaching methods and study load (number of hours): Participation in lectures (16); Preparation for lectures and exercises (24); Preparation of term paper (40); Total 80 hours.
Assessment methods
Evaluation methods and criteria: Preparation and defence of a term paper 100 %; Assessment: A 91 - 100; B 81 - 90; C 71 - 80; D 61 - 70; E 51 - 60; F 0 - 50.
Language of instruction
English
Further comments (probably available only in Czech)
The course is taught only once.
The course is taught: in blocks.
Note related to how often the course is taught: dva bloky po 9 hodinách v termínu 6. - 10. 11. 2023.
Information on the extent and intensity of the course: 18 hodin blokové výuky.
The course is also listed under the following terms Autumn 2023.
  • Enrolment Statistics (Autumn 2024, recent)
  • Permalink: https://is.muni.cz/course/econ/autumn2024/MPE_APRE