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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Optimization Methods in Finance (MATH11158)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting 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
Academic year 2023/24, Available to all students (SV1) 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 )
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 Finance2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Formulate and solve practical problems arising in finance using modern optimization methods and software (CVX, MATLAB).
  2. Demonstrate 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
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr Akshay Gupte
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
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