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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 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
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 2019/20, Available to all students (SV1) 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 )
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 Finance1:30
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 Andreas Grothey
Tel: (0131 6)50 5747
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
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