ICM204: Financial Econometrics

Module code: ICM204

Module provider: ICMA Centre; Henley Business School

Credits: 20

Level: 7

When you'll be taught: Semester 2

Module convenor: Professor Mike Clements, email: m.p.clements@icmacentre.ac.uk

Pre-requisite module(s): Students are expected to have taken an introductory econometrics module, at the level of ICM337, and to have some knowledge of Python, at the level of the content covered in ICM337 (Open)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: No placement specified

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 19 November 2024

Overview

Module aims and purpose

This module equips you with some of the more advanced econometric tools that might be used by market participants, and by researchers in finance and economics. It is an applied econometrics module that emphasises financial applications.  

The aims and objectives of the module are to enable the student to understand some of the more advanced econometric techniques used in the finance research literature. Via illustrations from the empirical finance literature and using an econometric software package, you will learn how to apply these techniques to real data. Emphasis is placed on critical application of the techniques. As such, the module aims to encourage the development of IT and data handling skills: in particular, the use of Python as the software to apply the econometric techniques to data, e.g. from Eikon and Bloomberg.

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

• explain the modern econometric approaches to the formulation, estimation and testing of multiple-equation models in time series and longitudinal contexts   
• formulate and apply econometric models to test for long-run relationships between variables 
• use models to forecast volatility 
• critically evaluate the use of econometrics in the published academic finance literature. 

Module content

1. Univariate time-series modelling  
2. Forecasting time series 
3. Simultaneous equations models 
4. Vector autoregressive models 
5. Properties of time-series variables  – orders of integration 
6. Cointegration, single equation and systems approaches 
7. Volatility modelling and forecasting 
8. Maximum likelihood and likelihood ratio tests   
9. Panel data: individual and time effects, dynamic models  
10. Simulation methods – Monte Carlo and the Bootstrap 

Structure

Teaching and learning methods

The module uses a mixture of (1) lectures where the theory and concepts are introduced and (2) computer-lab based seminars where we apply the knowledge to practical cases and use Python as the econometric software.

Study hours

At least 28 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

50% weighted average mark

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Weekly practice MCQ tests via Blackboard

Reassessment

Additional costs