25879 Statistics and Financial Econometrics
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Subject handbook information prior to 2020 is available in the Archives.
Credit points: 8 cp
Subject level:
Postgraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Description
The subject reviews the necessary statistical and econometric tools for modelling financial data and describing stylised facts about asset returns. It also introduces students to statistical inference and model estimation for financial time series.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Define the main probability-theoretic and statistical concepts required in modern finance |
|---|---|
| 2. | Apply a range of statistical techniques to the analysis of financial data |
| 3. | Apply econometric techniques to estimate popular models using financial time series |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Access and critically analyse financial data to inform and facilitate effective decision making (1.1)
Contribution to the development of graduate attributes
The subject provides a foundation for a mathematical treatment of stochastic modelling. It contributes to the objectives of the course by focussing on the principles and methods of modern probability theory, and their application to financial data.
This subject contributes to the development of the following graduate attributes:
- Business knowledge and concepts
- Business practice oriented skills
This subject also contributes specifically to develop the following Program Learning Objectives for the Master of Quantitative Finance:
- 5.1: Master quantitative finance technical skills necessary for professional practice
Teaching and learning strategies
The subject is presented in seminar format. Essential principles are presented and analysed in the lecture component, after which students are guided through practical application exercises.
Content (topics)
- Random variables
- Univariate and multivariate distributions
- Copulas
- Principal component analysis
- Sampling
- Goodness-of-fit tests
- Maximum likelihood estimation
- Estimation of diffusion processes
- Regression analysis
Assessment
Assessment task 1: Assignments (Individual)
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 3 |
|---|---|
| Weight: | 50% |
Assessment task 2: Final Exam (Individual)
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1 |
|---|---|
| Weight: | 50% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Recommended texts
Hinz, Juri: Statistics and Financial Econometrics (Lecture Notes)
References
Gut, Allan: An Intermediate Course in Probability
Wackerly, D., Mendenhall, W. and Scheaffer: Mathematical Statistics with Applications
Brockwell, Peter J., Davis, Richard A.: Introduction to Time Series and Forecasting
Ivchenko, Medvedev, Chistyakov: Problems in Mathematical Statistics
Green, W., Econometric Analysis, 6th edition, Prentice Hall, 2008
Miller, M., and Miller, M., Mathematical Statistics, 6th edition, Prentice Hall, 1999
Rice, J., Mathematical Statistics and Data Analysis, 2nd edition, Duxbury Press, 1995
Ross, S. M., Introduction to Probability Models, 10th edition, Academic Press, 2010
