Throughout the 2021-22 academic year, we will be adapting the way we teach and assess your modules in line with government guidance on social distancing and other protective measures in response to Coronavirus. Teaching will vary between online and on-campus delivery through the year, and you should read the additional information linked on the right hand side of this page for details of how this will work for this module. The contact hours shown in the module information below are superseded by the additional information.You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.
All dates for assessments for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook at http://go.warwick.ac.uk/STassessmenthandbook
ST959-15 Financial Statistics
This module runs in Term 1 and aims to introduce the main approaches to statistical inference and financial time series.
Upon completing this module, students need to be able to analyse, explain and apply the statistical techniques to finance.
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Part 1: Classical and Bayesian methods of statistical inference (weeks 1-5)
- Properties of random samples
- Statistics, sufficiency and likelihood
- Point estimation, maximum likelihood estimation
- Hypothesis testing and interval estimation
- Elements of Bayesian inference
- Linear models
Part 2: Time Series (weeks 6-10)
- Auto-regressive and moving average models (ARMA), unit root (ARIMA) and seasonal models (S-ARIMA), heteroscedastic models (GARCH and extensions such as EGARCH, GARCH-M,...) and an introduction to stochastic volatility models.
- Linear and non-linear modelling of financial time series with R: exploratory analysis, model selection, model fitting, model validation and forecasting.
- Illustrative financial applications.
By the end of the module, students should be able to:
- Explain the different approaches of statistical inference for points estimation, hypothesis testing and confidence set construction.
- Apply linear models in general situations and perform ANOVA.
- Understand and critically analyse ARMA, unit root, S-ARIMA, and GARCH models. Apply these models to financial data and carry out relevant computations.
- Demonstrate an understanding of the generalised linear model, including an appreciation of the circumstances where it may or may not be applied and, where appropriate, good judgement of where to apply it.
Indicative reading list
- George Casella, Roger Berger: Statistical Inference, (2002) Cengage Learning; 2nd edition
- David Ruppert and David S. Matteson: Statistics and Data Analysis for Financial Engineering: with R examples, Springer; 2nd edition
- Larry A. Wasserman: All of Statistics: A Concise Course in Statistical Inference, Springer
- Jonathan D. Cryer and Kung-Sik Chan: (2008) Time Series Analysis: With applications in R, Spinger, 2nd edition
- David Ruppert and David S. Matteson: (2015) Statistics and Data Analysis for Financial Engineering: with R examples, Springer; 2nd edition
- Ruey S Tsay: (2010) Analysis of Financial times series, Wiley; 3rd edition
- Financial Econometrics by Christian Gourieroux and Joann Jasiak, Princeton University Press.
Subject specific skills
|Lectures||30 sessions of 1 hour (20%)|
|Practical classes||7 sessions of 1 hour (5%)|
|Private study||111 hours (74%)|
|Assessment||2 hours (1%)|
Private study description
Weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D1
Term 1 Week 7.
Term 2 Week 1.
|Locally Timetabled Examination||80%||2 hours|
~Platforms - Moodle
Assessment group R2
Duration 2 hours.
~Platforms - Moodle
Feedback on assessment
- Verbal qualitative feedback will be given after the class test.
- Written quantitative and qualitative feedback will be given after the final exam and the computational project.
There is currently no information about the courses for which this module is core or optional.