Postgraduate Course: Deterministic Optimization Methods in Finance (MATH11092)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	7.5	ECTS Credits	3.75
Summary	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. This course is only available to students on the MSc Financial Mathematics programme.
Course description	- 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

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations	Students MUST NOT also be taking Optimization Methods in Finance (MATH11110)	Other requirements	None

Course Delivery Information

Academic year 2015/16, Not available to visiting students (SS1)		Quota: None
Course Start	Semester 2
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 75 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 10, Summative Assessment Hours 1, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 52 )
Assessment (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment)	See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)	MSc Deterministic Optimization Methods in Finance		1:00

Learning Outcomes
Ability to formulate and solve practical problems arising in finance using modern optimization methods and software (CVX, MATLAB). Familiarity with deterministic formulations, their purpose, strengths and weaknesses.