## ICM204-Financial Econometrics

Module Provider: ICMA Centre
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites: ICM103 Quantitative Methods for Finance or REMF37 Quantitative Techniques
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Prof Mike Clements

Type of module:

Summary module description:

Building on the material introduced in Quantitative Methods 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.

Aims:

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, and test hypotheses, and to appreciate the challenges (and opportunities) of time-series data, and panels of data.

Assessable learning outcomes:

•  Describe, estimate and evaluate a number of different approaches to modelling and forecasting financial data

• Determine the appropriate class of model to address a particular problem in empirical finance

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

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 4 Practicals classes and workshops 4 Guided independent study: Wider reading (independent) 56 Exam revision/preparation 30 Advance preparation for classes 26 Carry-out research project 20 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 exam

Summative assessment- Coursework and in-class tests:

One Group Project (5-6 students), to be submitted last week of Spring term

Formative assessment methods:

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

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.