Financial and Computational Mathematics
Why Choose This Course
Employers in the banking and investment sector require graduates with a deep understanding of the relevant mathematical concepts as well as the practical and computational skills associated with applying them. This course provides both, and is an opportunity for students pursuing mathematical degrees that are not specifically financial in nature to continue to studies in advanced mathematics with a financial focus, and therefore enhance their employment options.
The course combines mathematical rigour with an emphasis on computational finance and the opportunity to study machine learning, a combination that is currently unique in Ireland. The opportunity to work in teams is built into the programme structure.
UCC itself enjoys proximity to financial employers in Ireland (for the example in the IFSC in Dublin) and in other European financial centres, including London. Students applying from overseas may also wish to consider Ireland's Third Level Graduate Scheme, details of which are available at http://inis.gov.ie/en/INIS/Pages/Student%20Pathway.
Modern finance is increasingly reliant upon advanced mathematical and computational techniques for the modelling of asset and financial market movements, the design and valuation of financial derivatives, and portfolio management.
This course provides an appropriately rigorous treatment of branches of mathematics applicable to financial modelling, including measure-theoretic probability, stochastic processes in discrete and continuous time, and partial differential equations. It is mathematically challenging and requires prior familiarity with multivariate calculus, differential equations, linear algebra, probability, and statistics. You should also have some experience of programming.
The rapid increase in available computing speeds over the past fifteen years has led to the widespread adoption of sophisticated computational methods for financial modelling and the development of algorithmic approaches to market trading.
Computational methods form a core part of this course; we provide exposure to relevant software including Python, R and C#, and provide the option to study machine learning, which is emerging as an essential and rapidly developing tool in industry.
- Candidates must have obtained at least a 2.2 honours degree or equivalent in the mathematical sciences or another highly numerate discipline;
- Candidates who have obtained at least a 2.2 honours degree in Engineering or Physics may be considered but are expected to have sufficient background in university-level mathematics as assessed by the Course Co-ordinator;
- All candidates must be ultimately approved by the Course Co-ordinator.
English Language Requirements
Applicants that are non-native speakers of the English language must meet the university approved English language requirements available online.
For applicants with qualifications completed outside of Ireland
Applicants must meet the required entry academic grade, equivalent to Irish requirements, please find our grades comparison by country online.
For full details of the non-EU application procedure please visit our how to apply pages for international students. In UCC, we use the term programme and course interchangeably to describe what a person has registered to study in UCC and its constituent colleges, schools, and departments.
Not all courses are open to international/non-EU applicants, please check the fact file above.
For more information please contact the International Office.
A variety of assessment methods are used during this course. In addition to the traditional end-of-year written examinations, you will undertake a series of individual and group assignments and projects during the year in the areas of both management accounting and information systems.
In addition, an applied research project is undertaken in groups by all students during the second teaching term, culminating with a series of group presentations to leading industry experts who in turn provide invaluable feedback and advice to each student group.
The IS6215 Business Research and Communications Skills module (10 credits), which forms part of the course syllabus, is an invaluable tool for you in preparing for such presentations.
Core Module (45 credits)
MF6010 Probability Theory in Finance (10 credits)
MF6011 Derivatives, Securities, and Option Pricing (5 credits)
MF6012 Computational Finance I (5 credits)
MF6013 Computational Finance II (5 credits)
MF6014 Topics in Financial Mathematics (5 credits)
MF6015 Continuous-Time Financial Models (5 credits)
AM6004 Numerical Methods and Applications (5 credits)
CS6322 Optimisation (5 credits)
Elective Modules (Choose 15 credits)
AM4062 Applied Stochastic Differential Equations (5 credits)
AM6007 Scientific Computing with Numerical Examples (10 credits)
AM6019 Partial Differential Equations (5 credits)
ST4400 Data Analysis II (5 credits)
ST6040 Statistical Analytics Implementations I (5 credits)
ST6041 Statistical Analytics Implementations II (5 credits)
CS6503 Introduction to Relational Databases (5 credits)
MF6016 Dissertation in Financial and Computational Mathematics (30 credits)
Note: Module selection must be approved by the module co-ordinator.
Further details on the modules listed above can be found in our book of modules. Any modules listed above are indicative of the current set of modules for this course but are subject to change from year to year.
Start Date 7 September 2020
Post Course Info
Skills and Careers Information
Graduates will be prepared for quantitative roles in the financial services sector and particularly in investment banking, including roles in financial engineering, quantitative finance, investment analysis and fund management. The programme also serves as a route to further study in this area.