Postgraduate Course: Optimization Methods in Finance (MATH11158)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|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 9 weeks and 2 hours of labs in alternate weeks. 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.
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.
Entry Requirements (not applicable to Visiting Students)
||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?
Course Delivery Information
|Academic year 2021/22, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 18,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Hours & Minutes
|Main Exam Diet S2 (April/May)||MSc Optimization Methods in Finance||2:00|
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.
|Lecture notes and slides|
Optimization Methods in Finance, G. Cornuejols and R. Tütüncü, Cambridge University Press. ISBN-10: 0521861705
|Graduate Attributes and Skills
|Course organiser||Dr Akshay Gupte
|Course secretary||Miss Gemma Aitchison
Tel: (0131 6)50 9268