The programme will:
- provide a solid training in mainstream advanced statistical modelling with special focus on statistical finance
- expose students to modern developments in statistical finance
- reflect the research interests of the department in stochastic financial modelling.
At the end of the programme students should:
- be able to embark on a programme of research as a research student
- be able to undertake data analysis for a variety of statistical problems with special focus on financial data
- have learned key programming skills, both in data analysis and mathematical typesetting
- be equipped as a financial statistician for a range of careers in industry, commerce and the public sector
- have learned to express mathematical concepts and statistical analysis in both written and verbal form.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates must enroll on exactly 180 or 185 credits overall, with at least 135 credits at level 5M. Please note that in order to obtain the MSc award students need to pass 150 credits with at least 135 at level 5 with a minimum classification average of 5.0 . Please refer to the 'rules of award' document for further details, with particular attention to section 16.
Students will be awarded the PGCert if they exit with 60 credits (including 45 at Level 5M), or the PGDip if they exit with 90 credits (including 75 at Level 5M).
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH5320M | Discrete Time Finance | 15 | Semester 1 (Sep to Jan) | |
| MATH5330M | Continuous Time Finance | 15 | Semester 2 (Jan to Jun) | |
| MATH5340M | Risk Management | 15 | Semester 2 (Jan to Jun) | |
| MATH5802M | Time Series and Spectral Analysis | 15 | Semester 1 (Sep to Jan) | |
| MATH5871M | Dissertation in Statistics | 60 | 1 Jun to 30 Sep, 1 Jun to 30 Sep | PFP |
Candidates will be required to study 60 to 65 credits from the following optional modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH3092 | Mixed Models | 10 | Semester 2 (Jan to Jun) | |
| MATH3820 | Bayesian Statistics | 10 | Not running in 202425 | |
| MATH3823 | Generalised Linear Models | 10 | Semester 2 (Jan to Jun) | |
| MATH5092M | Mixed Models with Medical Applications | 15 | Semester 2 (Jan to Jun) | |
| MATH5306M | Introduction to Programming | 5 | Semester 1 (Sep to Jan) | |
| MATH5350M | Computations in Finance | 15 | Semester 2 (Jan to Jun) | |
| MATH5714M | Linear Regression, Robustness and Smoothing | 20 | Semester 1 (Sep to Jan) | |
| MATH5772M | Multivariate and Cluster Analysis | 15 | Semester 1 (Sep to Jan) | |
| MATH5820M | Bayesian Statistics and Causality | 15 | Not running in 202425 | |
| MATH5824M | Generalised Linear and Additive Models | 15 | Semester 2 (Jan to Jun) | |
| MATH5825M | Independent Learning and Skills Project | 15 | Semester 2 (Jan to Jun) | |
| MATH5835M | Statistical Computing | 15 | Semester 1 (Sep to Jan) |
Last updated: 29/04/2024 16:07:40
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