Postgraduate Course: Optimization Methods in Finance (MATH11158)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course will demonstrate how recent advances in optimization modelling, algorithms and software can be applied to solve practical problems in computational finance. The focus is on selected topics in finance (such as arbitrage detection, risk-neutral probability measure, portfolio theory and asset management), where the models can be formulated as deterministic or stochastic optimization problems. These problems have various forms (e.g. linear, quadratic, conic, convex, stochastic optimization) and hence various tools, techniques and methods from optimization need to be employed to solve them numerically. An integral part of the goal of the course is to gain skills in detecting this so that the right algorithms and optimization methodology is applied. The course is designed as 2 hours of lectures for 11 weeks and 1 hour of a tutorial/workshop in alternate weeks. |
| Course description |
The optimization topics covered in this course include:
1. Linear, quadratic and conic optimization.
2. Mixed-integer optimization.
3. Optimization under uncertainty : stochastic, chance-constrained and robust optimization. Algorithms such as stochastic gradient descent and Benders¿ decomposition.
These will be studied in the context of financial applications such as asset pricing and arbitrage, portfolio optimization (Markowitz model and others), Sharpe ratio of portfolios, asset/liability management.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | Visiting students are advised to check that they have studied the material covered in the syllabus of each prerequisite course before enrolling. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2023/24, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Supervised Practical/Workshop/Studio Hours 5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
71 )
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| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework 50%
Examination 50% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | MSc Optimization Methods in Finance | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Formulate and solve practical problems arising in finance using modern optimization methods and software (CVX, MATLAB).
- Demonstrate familiarity with selected deterministic and stochastic formulations, their purpose, strengths and weaknesses.
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Reading List
Lecture notes and slides
Optimization Methods in Finance, G. Cornuejols and R. Tütüncü, Cambridge University Press. ISBN-10: 0521861705 |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | OMF |
Contacts
| Course organiser | Dr Akshay Gupte
Tel:
Email: |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: |
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