ICM204-Financial Econometrics

Module Provider: ICMA Centre
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Spring term module
Non-modular pre-requisites:
Co-requisites: ICM337 Econometric Analysis for Finance or REMF37 Quantitative Techniques
Modules excluded:
Current from: 2022/3

Module Convenor: Prof Mike Clements
Email: m.p.clements@icmacentre.ac.uk

Type of module:

Summary module description:

Building on the material introduced in Econometric Analysis for Finance, this module covers a number of more advanced techniques that are relevant for financial applications, and in particular for modelling and forecasting financial time series. These include an introduction to maximum likelihood estimation and two-stage least squares, models of volatility, simulation techniques, and multivariate models. Case studies from the academic finance literature are employed to demonstrate potential uses of each approach. Extensive use is also made of financial econometrics software to demonstrate how the techniques are applied in practice.


To provide students with a critical understanding of modern econometrics, with an emphasis on financial applications. To enable students to analyse data, estimate systems of equations for data which might be stochastically non-stationary, or simultaneously determined, and to model conditional variances. The aim is to appreciate the challenges (and opportunities) of time-series  and panels of data for discovering empirical “facts” about the economic and financial system. 

Assessable learning outcomes:

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

  • Apply  a number of different approaches to modelling and forecasting to financial data

  • Critically evaluate alternative models and methods for addressing  particular problems in empirical finance (e.g., forecasting), and to analyse any limitations of the approach

  • To be able to critically evaluate the use of econometrics in the published academic finance literature

Additional outcomes:

The module also aims to encourage the development of IT skills and in particular the manipulation of data using statistical software packages. Students will also improve their ability to translate abstract theoretical concepts into practical solutions to financial problems.

Outline content:

Topic 1 Univariate time-series modelling and forecasting

Topic 2 Simultaneous equations models

  • Simultaneous equations bias

  • Identification

  • Estimation, triangular systems

Topic 3 Vector autoregressive models 

  • Motivation, formulation, estimation

  • Comparison with structural models

  • Causality, impulse response functions, variance decompositions

Topic 4 Multivariate cointegration

  • the Johansen approach 

  • hypothesis testing using Johansen.

Topic 5 Volatility modelling and forecasting

  • Maximum likelihood estimation 

  • Volatility modelling using autoregressive conditionally heteroscedastic (ARCH) models 

  • variants and extensions of the ARCH model

Topic 6 Panel data analysis

  • fixed effects

  • random effects

  • Dynamic models  

Topic 7 Simulations methods in econometrics and finance

  • motivation

  • pure simulation versus bootstrap

  • variance reduction techniques

Brief description of teaching and learning methods:

Core lectures supported by lab based computer seminars and classroom based tutor led discussion

Contact hours:
  Autumn Spring Summer
Lectures 20
Seminars 7
Practicals classes and workshops 3
Guided independent study:      
    Wider reading (independent) 30
    Exam revision/preparation 30
    Advance preparation for classes 20
    Revision and preparation 20
    Carry-out research project 30
    Reflection 40
Total hours by term 0 200 0
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Written exam 60
Project output other than dissertation 40

Summative assessment- Examinations:

One 3 hour written exam

The examination for this module will require a narrowly defined time window and is likely to be held in a dedicated exam venue.

Summative assessment- Coursework and in-class tests:

One Group Project (4-5 students), to be submitted in the  last week of the Spring term or the first week of the Easter vacation. The maximum word count is 2,500.

Formative assessment methods:

Penalties for late submission:

In accordance with the University policy. 

Assessment requirements for a pass:
50% weighted average mark

Reassessment arrangements:

By written examination only, as part of the overall examination arrangements for the MSc programme.

Additional Costs (specified where applicable):

Last updated: 22 September 2022


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