BE333-6-AP-KS:
Empirical Finance
2022/23
Essex Business School
Kaplan Singapore
Autumn & Spring
Undergraduate: Level 6
Current
Thursday 06 October 2022
Friday 24 March 2023
15
17 October 2022
Requisites for this module
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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 to run models with real financial data.
• To familiarise students with techniques for modelling financial data
• To build a bridge between financial theories and practice
• To introduce students to time series analysis with financial applications
On successful completion of the module, students will be able to:
• 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.
• Set up a multiple regression model and perform variable/ model selection.
• Work with non-linear but linearisable data generating processes and estimate models
based on them.
• 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.
• 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.
• Understand the concept of non-stationarity (unit root processes) and cointegration and
when to use the related estimation methods.
• Set up a time series volatility model (ARCH, GARCH, GJR, EGARCH).
• Achieve proficiency in econometric software
Skills for Your Professional Life
(Transferable Skills - reflecting the skills mapping recently undertaken in EBS)
The module is geared towards building up or enhancing the following transferable skills:
* Proficiency in estimating econometric models
* The ability to interpret in detail model output and write up research / technical reports on it
* Make useful contributions at model-building stage within a team setting when dealing
with econometric estimation
* 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
Analytical Domain
Academic and Cognitive Skills
A1 Synthesis and bringing together concepts and ideas
A2 Critical thinking
A3 Evaluation of evidence
A4 Creative problem solving
Data Anlysis Skills
D1 Analysing quantitative data
D3 Anaysing financial data
D4 Analysing Academic literature
Communication Skills
C2 Expressing research findings in report or slide deck
C5 Argumentation / Essay writing skills
Professional Practice Domain
Technology Skills
T1 Core IT skills (word, excel, PowerPoint, outlook)
T2 Working with data interactively (dashboards, databases)
T3 Understanding specialist software for your subject including programming and project management tools
T4 Understanding data visualisation and manipulation using industry benchmark software
Finance Skills
F1 Understand concepts and methodologies used to explain the behaviour of different financial market participants
and the functioning of different financial market types
F2 Apply key concepts and methodologies used to explain the behaviour of different financial market participants
and the functioning of different financial market types
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.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Exam |
Reassessment Main exam: Remote, Open Book, 120 minutes during September (Reassessment Period)
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.
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