Empirical Finance

The details
Essex Business School
Colchester Campus
Undergraduate: Level 6
Thursday 03 October 2024
Friday 13 December 2024
23 June 2023


Requisites for this module
BE314 or EC252



Key module for

BSC N300 Finance,
BSC N301 Finance (Including Foundation Year),
BSC N302 Finance (Including Year Abroad),
BSC N304 Finance (Including Placement Year)

Module description

This module builds on the second-year module, BE314 Financial Modelling, to deepen students' understanding of linear and non-linear regression models.

Module aims

The aims of this module are:

  • 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.

Module learning outcomes

By the end of this module, students will be expected to be able to:

  1. Set up an OLS model to estimate a linear relationship between a set of variables.

  2. Be able to interpret all items in an OLS output.

  3. Set up a multiple regression model and perform variable/ model selection.

  4. Work with non-linear but linearisable data generating processes and estimate models based on them.

  5. 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.

  6. 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.

  7. Understand the concept of non-stationarity (unit root processes) and co-integration and when to use the related estimation methods.

  8. Set up a time series volatility model (ARCH, GARCH, GJR, EGARCH).

  9. Achieve proficiency in Eviews.

Skills for your professional life (Transferable Skills)

This 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 policymakers.

Module information

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.

Learning and teaching methods

This module will be delivered via:

  • One 2-hour lecture per week.
  • One 1-hour computer lab per week.

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.


  • Brooks, Chris. (2019) Introductory econometrics for finance, Cambridge: Cambridge University Press.
  • 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.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Research Report 1    50% 
Coursework   Research Report 2    50% 

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

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Christos Argyropoulos, email:
Dr Christos Argyropoulos



External examiner

Dr Hf Guo
University of Durham
Assistant Professor in Finance
Available via Moodle
Of 67 hours, 67 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.


Further information
Essex Business School

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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