The University of Southampton
Courses

MANG6297 Advanced Time Series Modelling

Module Overview

The module offers a comprehensive introduction to Advanced Time Series Modelling. You will learn various analytical tools to enable you to analyse financial data. The module expects prior skills in data analysis covered by the module Quantitative Finance (MANG6299) in the 1st semester. In addition, Students on the MSc Risk and Finance can choose this module but they will need to have taken MANG6003 Quantitative Methods in the first semester if they wish to do so.

Aims and Objectives

Module Aims

The purpose of this module is to provide the necessary skills to conduct time series modelling and forecasting. 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, STATA).

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
  • competence in using an econometric software package (STATA)
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • analyse financial data
  • evaluate model fit
  • assess out-of-sample properties
  • interpret statistical output
  • relate forecasts to strategic decisions
  • critically evaluate statistical models and forecasting tools
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • analyse financial data
  • develop quantitative models

Syllabus

The module will introduce methods developed in time series analysis and apply these methods to financial data. The module will stress the relationship between finance, econometrics and statistics. The module will be also offered as an option on other programmes (i.e. MSc International Financial Markets, MSc International Banking and Financial Studies). The module will use comparative case studies and will analyse financial data in different settings (countries, industries and governance mechanism). Topics: 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 Multivariate time series modelling - Panel vector autoregression - Panel co-integration

Special Features

The assessment is aligned with the learning outcomes and objectives. You will analyse financial data and will develop statistical models in STATA. You will apply the methods and concepts discussed in the lecture to real cases and various datasets. This will include the assessment of financial statements, stock market data, quantitative modelling in STATA and discussing management implications. The module will focus more on the practical aspects of time series modelling and will highlight applications in practice.

Learning and Teaching

Teaching and learning methods

Teaching methods include: The module will be taught by a mixture of methods ranging from guided background reading, lectures, group work and the exploration of mini case studies and datasets. The lecturer will draw upon market developments current at the time of the course. The lecturer will introduce the concepts, and you will have the opportunity to practice and apply the methods discussed. We will do a step-by-step analysis of different financial data (stock market data, firm and industry-specific data). Learning activities include: - EVIEWS based exercises in class - A group assignment - Discussion of findings in class There will be many opportunities for you to gain feedback from your tutor and/or peers about your level of understanding and knowledge prior to any formal summative assessment such as coursework or examinations. In particular, class exercises and short presentations in class will provide an opportunity for feedback from peers and tutors.

TypeHours
Teaching24
Independent Study126
Total study time150

Resources & Reading list

Campbell, J.Y., Lo, A.W. & A.C. MacKinlay (1997). The Econometrics of Financial Markets. 

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

Chiang, A.C. (1984). Fundamental Methods of Mathematical Economics. 

Asterious, G. and S. Hall (2011). Applied Econometrics. 

Enders, W. (2014). Applied Econometric Time Series. 

Hayashi, F. (2000). Econometrics. 

Wooldridge, J.M. (2009). Introductory Econometrics. 

Assessment

Formative

Homework Exercises

Summative

MethodPercentage contribution
Examination  (2 hours) 50%
Group Assignment  (3000 words) 50%

Repeat

MethodPercentage contribution
Examination  (2 hours) 100%

Referral

MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

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:

Books and Stationery equipment

Students are expected to buy the core text.

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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