UNIVERSITY OF KENT
Module Specification
1.
Title:
Financial Econometrics (EC823)
2.
School:
Economics
3.
Start Date:
Session 2006-2007, revised November 2011
4.
Number of Students:
10 -15 students
5.
Withdrawn Modules:
New module.
6.
Level:
Level M (FHEQ Level: 7)
7.
Credits:
15 credits
8.
Terms Taught:
Spring term
9.
Pre/co-requisites:
EC8xx Financial Economics I
EC8xx Econometric Methods (formerly EC812 Quantitative Methods I).
10.
Programme of Study:
MSc in Economics and Finance - compulsory
MSc in Economics - optional
MSc in Economics and Econometrics - optional
MSc in International Finance and Development - optional
11.
Specific Learning Outcomes:
The module aims
to develop students understanding and ability to apply econometric techniques for the analysis
of financial time series as well as the empirical assessment of theoretical models of asset
prices and asset allocation;
to use an intuitive approach by use of practical examples and class based workshops using the
econometric packages such as Eviews, PcGive and PcGets;
to give participants the ability to critically evaluate empirical literature and carry out empirical
research.
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On successful completion of the module, students will
have a better understanding of asset pricing models and knowledge of econometric methods
used for modelling and forecasting financial time series;
be more critical in the reading and interpretation of empirical research;
have the ability to undertake unsupervised practical work using (for example) Eviews, PcGive
and PcGets;
be practiced in analysing financial time series using appropriate econometric techniques;
Minor Revision to module specification confirmed 28 September 2011
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be able to make oral presentations of empirical financial work;
have developed the ability to report applied econometric work in written form.
The quantitative analysis of financial data and the empirical evaluation of theoretical models are
crucial to the study and application of modern finance. The ability to apply econometric methods
also constitutes an essential part of modern postgraduate degree training in economics and
finance.
This module contributes substantially to the subject skills acquired across the MSc
programmes, specifically:
Economics MSc - A1-10; C1-10
Economics and Econometrics MSc – A1-10; C1-10
Economics and Finance MSc – A1-10; C1-10
International Finance and Economic Development MSc – A1-11; C1-12
12.
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Generic Learning Outcomes:
As regards general and transferable skills, the module will develop and reinforce student’s
skills in a number of different areas. In particular, students successfully completing this
module will have:
developed their ability to utilise modern computing resources to access and acquire financial
data from the Internet (and other available sources) and utilise standard PC software (currently:
Eviews, PcGive and PcGets) to generate written reports and undertake oral presentations;
acquired the ability to undertake modelling of economic behaviour and use statistical software
developed numeracy and problem solving skills from the interpretation and manipulation of
quantitative financial models;
presented economic arguments orally as well as in written form;
acquired the ability to undertake self-directed study involving the completion of a final written
report on an empirical project from within the general field of financial time-series.
This module contributes to most of the intellectual and transferable skills of the MSc
programmes, specifically:
Economics MSc - B1-6; D1-10
Economics and Econometrics MSc – B1-6; D1-10
Economics and Finance MSc – B1-6; D1-10
International Finance and Economic Development MSc – B1-6; D1-10
13.
Curriculum:
The module offers a research-oriented introduction to contemporary empirical finance by
linking econometric theory to empirical studies of asset markets. The course covers the
tools of financial econometrics and empirical finance with a moderate degree of
sophistication. The emphasis is on applications both in the literature and by the student.
Empirical illustrations are integrated through computer exercises with financial data using
(for example) Eviews, PcGive and PcGets, widely used econometric packages providing
many of the statistical tools for the analysis of financial time series.
The specific topics dealt with include:
Introduction to Time Series Econometrics
 Financial time series and their characteristics;
 Stochastic processes;
 Linear time series models: Autoregressions & Moving Average processes;
 Unit roots;
 Predicting stochastic processes;
 Estimating times series models.
Topics in Financial Econometrics
Minor Revision to module specification confirmed 14 December 2011
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Modelling and predicting the time-varying volatility of asset returns
o Generalized AutoRegressive Condition Heteroskedasticity (GARCH);
o Integrated GARCH models
o Non-Gaussian and asymmetric GARCH models
o Alternatives: Stochastic, realised and implied volatility.
o Multivariate GARCH models;
Measuring risk using Value-at-Risk (VaR);
Econometrics of asset pricing.
14.
Indicative Reading List:
Financial econometrics is a rapidly expanding area of econometric theory and application.
While there will be no single text which covers the whole module, the following textbooks
may be used in combination with research papers:
 Campbell, J.Y., A.W. Lo, and A.C. MacKinlay (1997), The Econometrics of Financial
Markets, Princeton: Princeton University Press.
 Cochrane, J. (2001), Asset Pricing, Princeton: Princeton University Press.
 Enders, W. (2010) Applied Economics Time Series 3rd edition. New York: Wiley.
 Franses, P.H., and D. van Dijk (2000), Non-linear time series models in empirical finance,
Cambridge: Cambridge University Press.
 Gourieroux, C., and J. Jasiak (2001), Financial Econometrics: Problems, Models and
Methods, Princeton: Princeton University Press.
 Hamilton, J. D. (1994), Time Series Analysis. Princeton: Princeton University Press.
 Mills, T.C. (1999), The Econometric Modelling of Financial Time Series, Cambridge:
Cambridge University Press.
 Taylor S.J. (2005), Asset Price Dynamics, Volatility, and Prediction, Princeton: Princeton
University Press.
 Tsay, R.S. (2005), Analysis of Financial Time Series, New Jersey: John Wiley & Sons, 2nd
edition.
15.
Learning and Teaching Methods:
The lectures introduce the module material and provide an overview of the principles of
macroeconometric techniques (24 hours). Applications of these techniques are conducted
in computing workshops using real world data (6 hours). Seminars will be used to facilitate
discussion and review the material taught in the lecture by tackling theoretical exercises (6
hours). Because of the overlap with the subject matter of empirical macroeconomics and to
facilitate the necessary bridge between material taught in Econometric Methods (EC821)
and econometric requirements in Empirical Macroeconomics (EC820), eight of the lecture
sessions on time series models and methods will be joint. It should be noted that the total
number of separate direct teaching hours exceeds those in most other Economics MSc
modules.
The seminar and class programme improves the analytical abilities of students, their
understanding of the module material and their communication skills. The seminars and classes
give the opportunity for each student to show his/her understanding of the module material and
ask questions about topics they are not sure about. Advice and feedback on communication
skills are also given. The lectures and computer workshops are designed to improve the
analytical and problem solving skills of students, and develop their ability to apply their
knowledge and understanding of financial econometric issues to real world data. Throughout
the module, emphasis is put on the need for students to improve their own learning skills and
academic performance. This is achieved through feedback on student work and academic
guidance on private study. The total anticipated work load is 150 hours.
16.
Assessment Methods:
The final mark for the module is comprised of 40% in-term coursework plus 60% end-ofyear examination. The coursework is in two equally weighted parts; a small computer-based
empirical project (30% of the final module mark) and a class test (10% of the final module
Minor Revision to module specification confirmed 14 December 2011
mark).
The coursework assesses all the non-oral specific and generic learning outcomes. Each of
the two assessments will test and develop most of the specific and general skills and
learning outcomes identified in sections 11 and 12. The computer-based exercise will
involve using econometric packages like Eviews or PcGive to undertake econometric
modelling of a relevant applied macroeconomic issue, and will test students’ analytical
skills, their ability to formulate methods of analysis to investigate macro level data, and their
ability to deal with practical problems and demonstrate an understanding of relevant issues
that arise in applied financial work. Each student will face a unique data set.
An end of year two–hour examination will consist of two equally weighted parts of
theoretical and applied exercises. The theoretical part reconsiders the econometric theory
material taught in the lectures and assessed in the class test. The applied part requires
students to discuss empirical estimation and test results produced by the computational
software used throughout the module within the conceptual context of the module. The
examination is designed to test and develop the non-computing skills and learning
outcomes identified in sections 11 and 12.
17.
Implications for Learning Resources:
Library:
New textbooks and manuals required.
Computing:
Additional facilities required for computing classes.
Access to financial data bases (like Datastream) is essential for projects.
Staff:
The school is satisfied that the necessary teaching resources are available.
Timetable:
New two-hour slot plus six additional computing classes and a joint four-hour
econometrics seminar in week 25.
Convenor:
Prof Hans-Martin Krolzig.
18.
Disability Implications:
As far as can be anticipated reasonably, the curriculum, learning and teaching methods and
forms of assessment do not present any non-justifiable disadvantage to students with
disabilities. Clearly individual cases may need to be drawn to the attention of the Director of
Learning and Teaching. The current teaching provision satisfies the University’s present
accessibility requirements.
Statement by the Director of Learning and Teaching: "I confirm I have been consulted on the
above module proposal and have given advice on the correct procedures and required content of
module proposals"
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Director of Learning and Teaching
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Date
Statement by the Head of School: "I confirm that the School has approved the introduction of the
module and will be responsible for its resourcing"
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Head of School
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Date
Revised June 2006
Minor Revision to module specification confirmed 14 December 2011