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MANG2075 Financial Econometrics 2

Module Overview

Financial Econometrics 2 builds on Financial Econometrics 1 and provides you with more skills to undertake empirical research in finance. Lectures will introduce important topics such as unit roots, stationarity, VAR models as well as a broad range of volatility models. You get also get the chance to conduct your own empirical research through EViews software in the tutorials which will take place in labs.

Aims and Objectives

Module Aims

to build on the module Financial Econometrics 1 and further your knowledge of empirical techniques in finance. Using problem-based learning methods, you will apply statistical methods to analyse financial datasets. Hence, the main learning objective is to enable you to understand and apply statistical methods using statistical software packages (EVIEWS).

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • econometric modelling
  • forecasting of financial time series
  • analyse financial data
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • analyse financial data and solve complex problems
  • critically evaluate statistical models and forecasting tools
  • use an econometric software package (EViews)
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • collect and analyse quantitative data

Syllabus

Classical time series analysis Deterministic trends Cyclicality Seasonality ARIMA models Box-Jenkins approach Forecasting Out-of-sample properties Structural breaks Testing for structural breaks Endogenous and exogenous tests Vector autoregression (VAR) Short-term dynamics Lag specification Forecasting Co-integration and long-term equilibrium Johansen procedure Structural breaks in long-term equilibrium Vector error correction models (VECM) Speed of adjustment Short and long-term dynamics Modelling conditional volatility ARCH model GARCH model

Learning and Teaching

Teaching and learning methods

Weekly lectures will provide an overview of the main issues arising in the module. Weekly classes will supplement the lectures which will support student learning by providing opportunities for you to attempt, and gain feedback on, numerical and problem-solving exercises. This will include access to computer labs to conduct primary research via EViews software as well as in the Bloomberg suite to access and analyse real-time financial data.

TypeHours
Revision60
Follow-up work24
Lecture24
Preparation for scheduled sessions10
Tutorial10
Wider reading or practice22
Total study time150

Resources & Reading list

Chris Brooks (2014). Introductory Econometrics for Finance. 

Marno Verbeek (2012). A Guide to Modern Econometrics. 

Greene, W.H. (2000). Econometric analysis. 

Assessment

Formative

In-class activities

Summative

MethodPercentage contribution
Examination  (2 hours) 50%
Project  (2000 words) 50%

Repeat

MethodPercentage contribution
Examination  (2 hours) 50%
Project  (2000 words) 50%

Referral

MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

MANG2XXX Financial Econometics 1

Costs

Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Printing and Photocopying Costs

There will be additional costs for printing.

Textbooks

Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the core/recommended text as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.

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