2017/18 Taught Postgraduate Module Catalogue

MATH5360M Optimisation Methods for Finance

15 creditsClass Size: 70

Module manager: Dr Elena Issoglio; Dr James Fung
Email: E.Issoglio@leeds.ac.uk; J.C.L.Fung@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2017/18

Pre-requisite qualifications

The qualifications to gain entrance to the MSc in Financial Mathematics are sufficient.

This module is not approved as an Elective

Objectives

To provide students with the analytical and numerical skills required to solve optimisation and derivative pricing problems in finance.

Learning outcomes
On completion of this module, students will be able to:
- write algorithms for solution of mathematical and finance related tasks;
- write simple programmes for solution of mathematical and finance-related tasks;
- explain in detail convex sets and functions, constrained and unconstrained maximisation problems, global and local extrema;
- describe linear programming problems;
- demonstrate an understanding of Lagrange multipliers;
- solve linear programming problems numerically;
- describe quadratic programming problems;
- demonstrate an understanding of numerical algorithms forsolving quadratic programming problems;
- solve mean-variance optimisation problems;
- demonstrate an understanding of simple stochastic programming problems;
- solve simple asset-liability management problems;
- apply optimisation methods in risk management.

Skills outcomes
Computer programming, algorithms, numerics, optimisation techniques, applications in portfolio and risk management

Syllabus

Portfolio choice, risk management and pricing of financial derivatives require solving optimisation problems. The module will develop the relevant mathematical tools, numerical methods and programming skills for analysing and solving optimisation problems in finance.

The module covers linear, quadratic and stochastic programming. Practical applications include arbitrage-free pricing of options, optimisation of risk measures, calculation of optimal portfolios, applications to asset-liability management and risk management. The module provides an introduction to a programming language.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	10	2.00	20.00
Practical	10	1.00	10.00
Tutorial	10	1.00	10.00
Private study hours			110.00
Total Contact hours			40.00
Total hours (100hr per 10 credits)			150.00

Private study

4 hours per lecture
2 hours per tutorial
1 hours per practical

40 hours Preparation for assessment

Opportunities for Formative Feedback

Progress will be monitored by contributions made to tutorials and during practicals; performance in ACWs.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course Assessment	.	40.00
Total percentage (Assessment Coursework)		40.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	2 hr	60.00
Total percentage (Assessment Exams)		60.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 16/06/2017

Disclaimer

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