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Financial Modelling with R - MAN00037I

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  • Department: The York Management School
  • Credit value: 20 credits
  • Credit level: I
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

The aim of this module is to provide a sound grounding on the analysis and modelling of financial data informed by financial theory. The module provides the opportunity for students to master the use of the statistical modelling language R on performing simulations and analyse economic and financial time series data.

Module learning outcomes

After successful completion of the module, students will be able to:

Subject content

Demonstrate and interpret statistical properties typically found in financial data (stylized facts)

Explain the main concepts of ARMA and ARIMA models

Define and estimate from data linear and non-linear time series models used in finance

Perform stochastic simulation to estimate financial quantities and indicators

Explain and fit financial factor models to data, and give interpretation of results

Use R to conduct statistical analyses and modelling of financial data

Academic and graduate skills

Present analyses in a logical, rigorous and concise way

Perform and demonstrate logical reasoning from assumptions to conclusion

Critically assess the assumptions underlying analyses and conclusions

Module content

Syllabus:

  • Stochastic simulation

  • Introduction to financial data

  • Classical time series models

  • Factor models of asset returns

  • Volatility models for financial asset returns

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 40
Closed/in-person Exam (Centrally scheduled) 30
Essay/coursework 30

Special assessment rules

None

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 40
Closed/in-person Exam (Centrally scheduled) 30
Essay/coursework 30

Module feedback

Feedback will be given in accordance with the University Policy on feedback in the Guide to Assessment as well as in line with the School policy.

Indicative reading

Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. Available from OTexts.com/fpp2. Accessed on 20 May 2022.


Zivot, E., Introduction to Computational Finance and Financial Econometrics with R. Available from bookdown.org/compfinezbook/introcompfinr. Accessed on 20 May 2022.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.