Course co-ordinator(s): Akshay Gupte (Edinburgh).
Aims:
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.
Summary:
- Linear Optimization: asset pricing and arbitrage, risk-neutral probability measure
- Quadratic Optimization: mean-variance portfolio selection (Markowitz model)
- Conic Optimization: capital allocation line and Sharpe ratio
- Stochastic Optimization: Asset/liability management, stochastic gradient descent, scenario generation
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh.
Semester: 2.
Learning Outcomes: Subject Mastery
- 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.
Reading list:
- Lecture notes and slides
- Optimization Methods in Finance, G. Cornuejols and R. Tütüncü, Cambridge University Press. ISBN-10: 0521861705
SCQF Level: 10, 11.
Credits: 15.
Other Information
Help: If you have any problems or questions regarding the course, you are encouraged to contact the course leader.
Canvas: further information and course materials are available on Canvas