Essex Business School
Autumn & Spring
Undergraduate: Level 6
Thursday 08 October 2020
Friday 26 March 2021
08 January 2020
Requisites for this module
This module builds on the second-year module, BE314 Financial Modelling, to deepen students' understanding of linear and non-linear regression models. The emphasis is on times series modelling addressing the pitfalls of Ordinary Least Squares (OLS) and problems with data. Problems and issues frequently encountered in practice, such as non-linear data generating processes, heteroscedasticity, autocorrelation and unit roots are examined.
In each case, we start off by defining the problem at hand, move on to how OLS results and prediction might be affected if the problem goes undetected, discuss the commonly employed tests for detection, and end with a discussion of corrective action.
We further enrich our modelling skills by building univariate time series models aimed at both the mean and the volatility of the series at hand. The context is empirical in nature sourcing topics from the Empirical Finance literature. We make extensive use of econometric analysis software Eviews to run models with real financial data.
The module aims to:
1. To familiarise students with techniques for modelling financial data
2. To build a bridge between financial theories and practice
3. To introduce students to time series analysis with financial applications
On successful completion of the module, students will be able to:
1. Set up an OLS model to estimate a linear relationship between a set of variables. Be able to interpret all items in an OLS output.
2. Set up a multiple regression model and perform variable/ model selection.
3. Work with non-linear but linearisable data generating processes and estimate models based on them.
4. Describe in detail the problems such as heteroscedasticity and autocorrelation that have to be dealt with when performing OLS estimation. Be able to perform diagnostic tests and take corrective actions.
5. Set up a time series model. Describe in detail how non-stationarity affects estimation. Be able to detect and correct for non-stationarity in the data.
6. Understand the concept of non-stationarity (unit root processes) and co-integration and when to use the related estimation methods.
7. Set up a time series volatility model (ARCH, GARCH, GJR, EGARCH).
8. Achieve proficiency in Eviews.
The module is geared towards building up or enhancing the following transferable skills:
1. Proficiency in estimating econometric models using Eviews.
2. The ability to interpret in detail model output and write up research / technical reports on it.
3. Make useful contributions at model-building stage within a team setting when dealing with econometric estimation.
4. Ability to interpret the econometric results intuitively by relating them to the theoretical, institutional and policy framework of the financial firms/markets/organisations with a view to make statistical results plausible as well as appealing to investors, practitioners and policy makers.
Contact time consists of a two-hour lecture per week for ten weeks and a one-hour computer lab per week for nine weeks. Labs are absolutely essential for the learning process and the value added of this module. Students are expected to do the relevant reading and preparation before each lecture and lab.
- Chris Brooks. (2019) Introductory econometrics for finance, New York, NY: Cambridge University Press.
The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.
Module supervisor and teaching staff
Prof Ekaterini Panopoulou, email: email@example.com.
Prof Ekakaterini Panopoulou
No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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