Financial Modelling Techniques
- Module Code:
- FHEQ Level:
- Year of study:
- Year 1
- Taught in:
- Term 1
This course provides an introduction to the applications of econometric methods in finance. It examines how one can formulate the relationships suggested by financial and economic theory a in mathematical and statistical format, how to estimate these models using sample data, and how to make relevant statements based on the parameters of the estimated models. The course examines the assumptions that are necessary for the estimators to have desirable properties and the assumptions necessary to make statistical inference. In addition, the course explores what happens when these assumptions are not satisfied, and what one can do in these circumstances. The course concludes with an examination of how to select an appropriate model for applications in finance.
Objectives and learning outcomes of the module
- Understand the principles of regression analysis in finance
- Use the program R to estimate a regression equation for bivariate and multiple regression models
- Test hypotheses concerning model parameters
- Discuss the consequences of multicollinearity, the methods for identifying multicollinearity, and how to deal with it
- Understand what is meant by heteroscedasticity and the consequences for OLS estimators and prediction based on those estimators
- Assess the methods used to identify heteroscedasticity, including data plots and more formal tests, and the various methods to deal with heteroscedasticity
- Understand autocorrelation, and discuss the consequences of autocorrelated disturbances for the properties of OLS estimator and prediction based on those estimators
- Assess the consequences of disturbance terms not being normally distributed
- Discuss the main criteria for the selection of an appropriate statistical model in financial applications
This module consists of a 2-hour weekly lecture over 10 weeks of term plus a revision lecture in term 3 as preparation for the final examination. Students will be supplied with a syllabus with a breakdown week by week of required and additional reading. Reading materials are usually accessed electronically from the BLE and students should come to class prepared.
This module also has a weekly 1-hour tutorial where the questions posed by the tutor relevant to the lecture are explored and discussed by the students. Students also prepare and deliver a short presentation.
Total Work load:
Students on this module will have 3 taught hours each week. Additionally, adequate personal study time should be allocated for reading and class preparation.
Scope and syllabus
1. A Review of Statistical Concepts
2. An Introduction to Econometrics and Regression Analysis
3. The Classical Linear Regression Model
4. Hypothesis Testing
5. The Multiple Regression Model
8. Non-normal Disturbances
9. Model Selection and Course Summary Contents
Method of assessment
Assessment for this module is in three elements:
- One tutorial presentation at 10%
- One essay of 2,500 words at 30%
- One unseen 2-hour written examination at 60%
All elements except the presentation may be resubmitted.