MSc Financial Mathematics
University College Dublin
Key Information
Campus location
Dublin, Ireland
Languages
English
Study format
On-Campus
Duration
1 year
Pace
Full time
Tuition fees
EUR 8,790 / per year **
Application deadline
Request info *
Earliest start date
Request info
* Rolling - Courses will remain open until such time as all places have been filled, therefore early application is advised
** EU fee per year - € 8790; non-EU fee per year - € 21460
Introduction
The MSc in Financial Mathematics is designed for students with an undergraduate degree in Mathematics or a 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 relevant contemporary knowledge and skills, including new digital innovations such as machine learning with financial applications, computational finance and statistical and data analysis. In the Summer Trimester, students will explore their theoretical and applied knowledge in greater depth by completing a dissertation or they will be able to apply their theoretical knowledge to real-world situations via a work placement with a financial firm. Among our industry partners, we list: AIB, FINCAD (Zafin Capital Markets Group) and Grant Thornton Advisory, Quantitative Risk.
Course Description
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 the 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.
Ideal Students
Who should apply?
Full-Time option suitable for:
- Domestic (EEA) applicants: Yes
- International (Non-EEA) applicants currently residing outside of the EEA Region: Yes
Admissions
Curriculum
Core Modules
- Stochastic Calculus
- Advanced Financial Models
- Counterparty Credit Risk
- Financial Risk Measurement and Management
Optional Module
- Computational Finance
- Statistical Machine Learning
- Optimization for Machine Learning
- Data Programming with Python
- Data Programming with R
- Introduction to Relational Databases and SQL Programming
- Measure Theory and Integration
- PDEs in Financial Maths
- Big Data Programming
- Advanced Econometrics: Time Series
- Behavioural Economics
- Energy Economics and Policy
Summer Modules
- Financial Dissertation
- Financial Work Placement
Program Outcome
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.
Program Tuition Fee
Career Opportunities
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