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Centre for Financial and Management Studies (CeFiMS)

Financial Econometrics

Course Code:
C359
Unit value:

Introduction

We define financial econometrics as 'the application of statistical techniques to problems in finance'. Although econometrics is often associated with analysing economics problems such as economic growth, consumption and investment, the applications in the areas of finance have grown rapidly in the last few decades.

Financial markets and others generate vast amounts of data on asset returns, their volatility, and other financial variables in long and high-frequency time series. The ability to analyse market behaviour requires knowledge of the properties of time series and appropriate estimation methods. Since the early 1980s techniques for analysing time series, which exhibit auto-regression, have yielded important studies of financial markets, increasing our knowledge of financial variables’ volatility. The objective of the course is to extend your knowledge and equip you with methods and techniques that allow you to analyse these finance-related issues.

Before starting this course, we recommend that you first complete the course Econometric Analysis & Applications [C332].

Resources

Study Guide

You will receive a looseleaf binder containing eight units. The units are carefully structured to provide the main teaching, defining and exploring the main concepts and issues, locating these within current debate and introducing and linking the further assigned readings. The unit files are also available to download from the Virtual Learning Environment.

Textbook

Brooks, Chris (2008) Introductory Econometrics for Finance, Cambridge University Press.

Econometric Software

You should have already received a copy of EViews, the software package for university econometrics modules, with the module materials for the module Econometric Principles & Data Analysis. 

If you have not yet taken this module prior to studying Financial Econometrics, you will be sent the application and instructions on how to use it along with the rest of the study materials.

Readings

You will receive a Reader volume containing a selection of key academic articles which apply the techniques studied in the course to financial data.

Virtual Learning Environment

You will have access to the VLE, which is a web-accessed study centre. Via the VLE, you can communicate with your assigned academic tutor, administrators and other students on the module using discussion forums. The VLE also provides access to the module Study Guide and assignments, as well as a selection of electronic journals available on the University of London Online Library.

Objectives and learning outcomes of the course

By the end of this course you will be able to:

  • Understand the properties of financial returns
  • Formulate models and analyse the properties of models using matrix notation
  • Understand the principles of autoregressive time series models and evaluate their ability to forecast financial variables
  • Understand the principles of maximum likelihood, and use maximum likelihood estimation and hypothesis testing
  • Understand ARCH and GARCH models and be able to apply them to financial time series which display volatility clustering and asymmetry
  • Estimate Vector Autoregressive (VAR) models and interpret the results
  • Apply limited dependent variable methods.

Scope and syllabus

Unit 1: Statistical Properties of Financial Returns
  • 1.1 Introduction
  • 1.2 Calculation of Asset Returns
  • 1.3 Stylised Facts about Financial Returns
  • 1.4 Distribution of Asset Returns
  • 1.5 Time Dependency
  • 1.6 Linear Dependency across Asset Returns
  • Exercises | Answers to Exercises
Unit 2: Matrix Algebra, Regression and Applications in Finance
  • 2.1 Introduction
  • 2.2 Matrix Algebra: Some Basic Concepts and Applications
  • 2.3 OLS Regression Using Matrix Algebra
  • 2.4 Applications to Finance
  • Exercises | Answers to Exercises
Unit 3: Maximum Likelihood Estimation
  • 3.1 Introduction
  • 3.2 The Maximum Likelihood Function: Some Basic Ideas and Examples
  • 3.3 The Maximum Likelihood Method: Mathematical Derivation
  • 3.4 The Information Matrix
  • 3.5 Usefulness and Limitations of the Maximum Likelihood Estimator
  • 3.6 Hypothesis Testing
  • Exercises | Answers to Exercises
Unit 4: Univariate Time Series and Applications to Finance
  • 4.1 Introduction
  • 4.2 The Lag Operator
  • 4.3 Some Key Concepts
  • 4.4 Wold’s Decomposition Theory (Optional section)
  • 4.5 Properties of AR Processes
  • 4.6 Properties of Moving Average Processes
  • 4.7 Autoregressive Moving Average (ARMA) Processes
  • 4.8 The Box-Jenkins Approach
  • 4.9 Example: A Model of Stock Returns
  • 4.10 Conclusions
  • Exercises | Answers to Exercises
Unit 5: Modelling Volatility – Conditional Heteroscedastic Models
  • 5.1 Introduction
  • 5.2 ARCH Models
  • 5.3 GARCH Models
  • 5.4 Estimation of GARCH Models
  • 5.5 Forecasting with GARCH Model
  • 5.6 Asymmetric GARCH Models
  • 5.7 The GARCH-in-Mean Model
  • 5.8 Conclusions
  • Exercises
Unit 6: Modelling Volatility and Correlations – Multivariate GARCH Models
  • 6.1 Introduction
  • 6.2 Multivariate GARCH Models
  • 6.3 The VECH Model
  • 6.4 The Diagonal VECH Model
  • 6.5 The BEKK Model
  • 6.6 The Constant Correlation Model
  • 6.7 The Dynamic Correlation Model
  • 6.8 Estimation of a Multivariate Model
  • Exercises | Answers to Exercises
Unit 7: Vector Autoregressive Models
  • 7.1 Introduction
  • 7.2 Vector Autoregressive Models
  • 7.3 Issues in VAR
  • 7.4 Hypothesis Testing in VAR
  • 7.5 Example: Money Supply, Inflation and Interest Rate
  • Exercises | Answers to Exercises
Unit 8: Limited Dependent Variable Models
  • 8.1 Introduction
  • 8.2 The Linear Probability Model
  • 8.3 The Logit Model
  • 8.4 The Probit Model
  • 8.5 Estimation using Maximum Likelihood
  • 8.6 Goodness of Fit Measures
  • 8.7 Example: Dividends, Growth and Profits
  • 8.8 Multinomial Linear Dependent Variables
  • 8.9 Ordered Response Linear Dependent Variable Models (optional section)
  • Exercises | Answers to Exercises

Method of assessment

Students are individually assigned an academic tutor for the duration of the module, with whom you can discuss academic queries at regular intervals during the study session.

You are required to complete two Assignments for this module, which will be marked by your tutor. Assignments are each worth 15% of your total mark. You will be expected to submit your first assignment by the Tuesday of Week 5, and the second assignment at the end of the module, on the Tuesday after Week 8. Assignments are submitted and feedback given online. In addition, queries and problems can be answered through the Virtual Learning Environment.

You will also sit a three-hour examination on a specified date in October, worth 70% of your total mark. An up-to-date timetable of examinations is published on the website in April each year.