Postgraduate Course: Credit Risk Modelling (MATH11130)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | The aims of this module are:
- to introduce students to quantitative models for measuring and managing credit risks
- to provide students with a critical understanding of the credit risk methodology used in the financial industry
- to give students an appreciation of the regulatory framework in which the models operate
This course is delivered by Heriot Watt University and is only available to students on the MSc Financial Mathematics programme. |
| Course description |
- Introduction to credit risk: credit-risky instruments, defaults, ratings
- Merton's model of the default of a firm
- Common industry models (KMV, CreditMetrics,CreditRisk+)
- Modelling dependence between defaults with factor models
- Latent variable and mixture models of default
- The Basel II regulatory capital formula
- Calculating the portfolio credit loss distribution
- Large portfolio behaviour of the credit loss distribution
- Calibration and statistical inference for credit risk models
- Overview of the more common single-name and portfolio/basket credit derivatives
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | MSc Financial Mathematics students only. Students must not have taken Credit Risk Management MATH11061 |
Course Delivery Information
|
| Academic year 2019/20, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
150
(
Lecture Hours 30,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
115 )
|
| Additional Information (Learning and Teaching) |
Examination takes place at Heriot-Watt University.
|
| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
See 'Breakdown of Assessment Methods' and 'Additional Notes', above. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Credit Risk Modelling (MATH11130) | 2:00 | |
Learning Outcomes
On completion of this module the student should be able to:
- Demonstrate an understanding of the nature of credit risk
- Describe the theoretical underpinnings of models used in the financial industry
- Show a knowledge of the regulatory framework and, in particular, the Basel II regulatory capital formula
- Describe how dependence is modelled in credit portfolios
- Describe mixture models of default and derive their mathematical properties
- Describe and use methods for calculating the portfolio loss distribution
- Describe and apply statistical approaches to calibrating credit risk models
- Explain the features and uses of the most common single-name products and basket derivatives
- Show an appreciation of the interface between academic theory and industrial practice
- Show an appreciation of the societal role of risk management in protecting the consumer and other stakeholders
- Demonstrate the ability to learn independently and as part of a group
- Manage time, work to deadlines and prioritise workloads
- Demonstrate skills in the understanding and processing of numerical information and interpretation of statistics
- Show knowledge of appropriate software for implementing solutions
|
Reading List
McNeil, A.J. and Frey, R. and Embrechts, P. (2005). Quantitative Risk Management: Concepts, Techniques and Tools. Princeton, New Jersey.
Bluhm, C. and Overbeck, L. and Wagner, C. (2002). An Introduction to Credit Risk Modeling. Chapman & Hall/CRC Financial Mathematics Series, London. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | CRMo |
Contacts
| Course organiser | Dr David Siska
Tel: (0131 6)51 9091
Email: D.Siska@ed.ac.uk |
Course secretary | Miss Sarah McDonald
Tel: (0131 6)50 5043
Email: sarah.a.mcdonald@ed.ac.uk |
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