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School of Social Sciences

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BAEcon Economics
Learn how the social sciences can help you to understand today's world.

BAEcon Economics

Year of entry: 2018

Course unit details:
Financial Econometrics

Unit code ECON31012
Credit rating 10
Unit level Level 3
Teaching period(s) Semester 2
Offered by Economics
Available as a free choice unit? Yes

Overview

See course Blackboard pages.

Pre/co-requisites

Unit title Unit code Requirement type Description
Mathematical Economics I ECON20120 Pre-Requisite Compulsory
Time Series Econometrics ECON30401 Co-Requisite Compulsory
ECON31012 Co-requisite: ECON30401 Time Series Econometrics

Aims

The aim of this course unit is to establish the foundations and principles of modern financial econometrics providing students with appropriate econometric techniques for empirical investigation in finance and financial economics. 

Learning outcomes

On completion of this unit you should be able to:

  1. Establish the specific characteristics of financial data.
  2. Undertake essential data handling tasks.
  3. Understand the features of univariate time series models.
  4. Understand the problem of volatility modelling and apply appropriate volatility modelling techniques.
  5. Model the relationship between multivariate variables.
  6. Undertake empirical analysis using econometric software (such as EViews).
  7. Use the acquired tools to read texts which introduce new methods.

Syllabus

One area in which time series econometrics is relied upon heavily is finance. Financial data are usually time series data and have rather unique statistical features. Importantly, they are also available in abundance. For instance you may have access to transaction data that give you second-by-second information. You will learn about the unique features of financial data and how high-frequency data can be used to model volatility and other aspects of return distributions.
 

At the core of financial econometrics is the issue of volatility modelling. It deserves its importance as volatility is frequently used as a proxy for risk. In that context, modelling volatility turns into modelling risk which has very obvious real life applications in finance. You will also learn about some alternative approaches to modelling risk, such as the celebrated conditional autoregressive value-at-risk model.


The multivariate versions of the volatility models allow the econometrician to estimate, and subsequently forecast, the correlation between several assets. This is a task of crucial importance as the value of correlations, to mention one application, determines the value of diversification.


This course unit will teach you the theoretical underpinnings of volatility and value-at-risk models as well as how to apply these using econometric software. Any student who is planning to apply their skills in the finance industry should consider choosing this course unit.


The course will cover the following topics.

  • Review of basic concepts of probability and statistics and univariate time series modelling with applications to financial data.
  • Univariate volatility modelling.
  • High frequency prices and realized volatility.
  • Value-at-Risk.
  • Forecast evaluation.
  • Multivariate volatility models.

Students may consult the 2016/17 lecture notes available at sites.google.com/site/oryschenko/teaching to get a better idea of the course content. However, please be aware that the content and presentation of the material may change.

Teaching and learning methods

Lectures and exercise classes.

Employability skills

Problem solving
To identify, analyse, and solve a problem applying the most appropriate techniques, and to understand the limitations of the proposed solutions.
Research
To plan, conduct, and report on independent research.
Written communication
To develop an accurate and succinct argument and to communicate it in writing.
Other
To develop efficient time management skills. Numerical and computer literacy.

Assessment methods

Method Weight
Written exam 80%
Project output (not diss/n) 20%

Feedback methods

  • Students will be given an opportunity to receive feedback on attempted exercises (7 problem sets) before and during the class in question.
  • Students will receive formative feedback on the empirical project before the final exam.
  • Students can also receive further feedback from classes, office hours, and revision sessions.

Recommended reading

Main reading includes (selected chapters from):

  • Chris Brooks (2014), Introductory Econometrics for Finance, 3rd ed., Cambridge University Press.
  • Michael P. Clements and David F. Hendry (eds), A Companion to Economic Forecasting, Blackwell Publishing, 2004.
  • Ruey S. Tsay (2013), An Introduction to Analysis of Financial Data With R, John Wiley & Sons.
  • and/or Ruey S. Tsay (2010), Analysis of Financial Time Series, 3rd ed., John Wiley & Sons.
     

Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 20
Practical classes & workshops 8
Independent study hours
Independent study 70

Teaching staff

Staff member Role
Vitaliy Oryshchenko Unit coordinator

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