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 modeling, 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 and 2 hours of MATLAB-based labs every week, in a continuous 4 hour session, during a 7 week period. The labs are hands-on and are aimed at building a practical skillset for solving realistic problems, and are related to the theoretical material covered in the lectures that day. |
| Course description |
1. Linear Optimization: asset pricing and arbitrage, risk-neutral probability measure
2. Quadratic Optimization: mean-variance portfolio selection (Markowitz model)
3. Conic Optimization: capital allocation line and Sharpe ratio
4. Stochastic Optimization: Asset/liability management, stochastic gradient descent, scenario generation
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2015/16, 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 14,
Supervised Practical/Workshop/Studio Hours 14,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
70 )
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| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| 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 | 1:30 | |
Learning Outcomes
- Ability to formulate and solve practical problems arising in finance using modern optimization methods and software (CVX, MATLAB).
- 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 Peter Richtarik
Tel: (0131 6)50 5049
Email: |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: |
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