Not available in 2020/21
FM457
Applied Computational Finance
This information is for the 2020/21 session.
Teacher responsible
Mr Alberto Pellicioli
Availability
This course is available on the MSc in Accounting and Finance, MSc in Finance (full-time), MSc in Finance (full-time) (Work Placement Pathway), MSc in Finance and Economics, MSc in Finance and Economics (Work Placement Pathway), MSc in Finance and Private Equity, MSc in Finance and Private Equity (Work Placement Pathway) and MSc in Quantitative Methods for Risk Management. This course is available with permission as an outside option to students on other programmes where regulations permit.
FM457A is intended for students taking FM442 Quantitative Methods for Finance and Risk Analysis and FM404 Forecasting Financial Time Series.
FM457B is available to students on the MSc Finance (Full-time), MSc Finance and Private Equity, MSc Finance and Economics and MSc Risk and Finance programmes.
Course content
Applied Computational Finance is a non-assessed, optional course intended to provide a solid foundation in the R numerical programming package. It does not assume students have any prior programming knowledge. It is based on the R/RStudio environment and basic programming concepts, moving on to libraries, functions, plotting, source code management, how to import data locally and via internet APIs, basic analysis and big data techniques. The course uses practical problems in finance for illustration, like risk analysis, price forecasting and derivative pricing.
Teaching
FM457A: 10 hours of seminars in the MT.
FM457B: 10 hours of seminars in the MT.
Indicative reading
Teaching notes will be distributed.
Assessment
This is an additional, non-assessed computer course to supplement MSc level courses in the Department of Finance.
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Key facts
Department: Finance
Total students 2019/20: Unavailable
Average class size 2019/20: Unavailable
Controlled access 2019/20: No
Value: Non-credit bearing
Personal development skills
- Problem solving
- Application of information skills
- Application of numeracy skills
- Commercial awareness
- Specialist skills
