Mathematics & Statistics - Financial Mathematics
MSc Financial Mathematics
Graduate Taught (level 9 nfq, credits 90)
The MSc in Financial Mathematics is designed for students with an undergraduate degree in Mathematics or related field, who wish to gain a competitive advantage in the financial sector by acquiring the strong mathematical and statistical background demanded by high-level quantitative roles. The proposed programme will equip students with the relevant contemporary knowledge and skills, including also new digital innovations such as machine learning and digital trading. In the Autumn and Spring Trimesters students will take modules in advanced mathematics with financial applications, computational finance and statistical and data analysis. In the Summer Trimester students will be able to apply their theoretical knowledge to real-world situations via a work placement with a financial firm, or explore their theoretical and applied knowledge in greater depth by completing a dissertation.
In the Autumn and Spring Trimesters, you will take a mixture of face-to-face and online modules (indicative module list below). In the Summer Trimester, you will have the opportunity to take up a summer work placement with a Dublin-based financial firm, or a dissertation supervised by a member of faculty. Upon completion of the programme, you will be able to understand, critique and judge the suitability of a number of advanced financial mathematical models, manipulate, analyse and discern the reliability of financial data sets, and be acquainted with industry practice.
Upon completion of the programme the students will be able to:
- demonstrate a deep knowledge of quantitative methodologies needed for jobs in investment banks and financial institutions.
- apply financial mathematical theory and quantitative methodologies to real world situations.
- critique and understand the limitations of financial mathematical models, judging the suitability of financial mathematical models and understand industry practice.
- write and run computer programmes that analyse complicated financial systems and data sets.
- analyse the reliability of a financial data set.
- generate new knowledge through research.
- access library and online resources to develop and understand financial mathematical theory and models.
- continue to study in a manner that may be largely autonomous.
- train others in the use of financial mathematical models.
Advanced Financial Models
Counterparty Credit Risk
Financial Risk Measurement and Management
Measure Theory and Integration
PDEs in Financial Maths
Applied Matrix Theory
Time Series Analysis – Act App
Data Prog with Python (online)
Data Prog with R (online)
Scientific Programming Concepts (ICHEC)
Mathematics of Machine Learning
Statistical Machine Learning (online)
Bayesian Analysis (online)
Categorical Data Analysis
Big Data Programming
Energy Economics and Policy
Financial Work Placement
The minimum entry requirement will be a 2:1 (or equivalent grade) BSc in Financial Mathematics, Mathematics, Applied and Computational Mathematics, or Statistics.
Applicants whose first language is not English must also demonstrate English language proficiency of IELTS 6.5 (no band less than 6.0 in each element), or equivalent.
Students meeting the programme's academic entry requirements but not the English language requirements, may enter the programme upon successful completion of UCD's Pre-Sessional or International Pre-Master's Pathway programmes. Please see the following link for further information http://www.ucd.ie/alc/programmes/pathways/
The following entry routes are available:
MSc Financial Mathematics FT (T341)
* Courses will remain open until such time as all places have been filled, therefore early application is advised
Full Time option suitable for:
Domestic(EEA) applicants: Yes
International (Non EEA) applicants currently residing outside of the EEA Region. Yes
1 year full-time
Post Course Info
Graduates with training in Financial Mathematics can cover upper-level quantitative roles in
several sub-sectors such as:
- Quantitative analysis in financial firms and hedge funds
- Risk modelling in banking and insurance
- Computational modelling in fintech
- Research and academia