SYLLABUS
A220A0000 - FINANCIAL ECONOMETRICS
Lecturer: Dr. Kashif Saleem
Associate Professor (Finance)
E-mail: kashif.saleem@lut.fi
Tel: +358 5 621 7284
Office: Room 7363.1, 3rd floor
Office hours: Tue. 13-14 pm (for course participants)
Course web: https://noppa.lut.fi/
Goals
The goal of the course Financial Econometrics is to deepen students’ knowledge on empirical
research methods in financial econometrics. The focus is on the empirical techniques used most
often in the analysis of financial markets and how they are applied to actual market data. The
course covers different areas of econometrics for example, classical linear regression model its
assumptions and how to deal with the violations of the assumptions of CLRM and their
diagnostics, univariate and multivariate time series analyses, modelling volatility and correlation,
GARCH family models and modelling long-run relationships in financial markets. Empirical
exercises using E-views is an integral part of the course, ensuring that the students acquire skills
and gain experience of data analysis in solving business and management problems.
Knowledge, skills and acquirements got by a student.
It is expected that by the end of the course the students will be able to:
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Conduct empirical tests on market efficiency
Test asset pricing models
Model time series by using ARMA process
Model volatility by using GARCH family models
Conduct correlation and co–integration analysis
Be able to efficiently conduct statistical and econometric analysis of data using the key
features and capabilities of the statistical software package Eviews.
Course material
Brooks, Chris: Introductory econometrics for finance. Cambridge, 2002 or newer.
Lecture Notes, Home Assignments, Data exercises will be available on NOPPA
Term paper: see sample paper and term paper questions for guideline
Term Paper groups: maximum 3 members (deadline to form a group: 10-09-2014, inform
me by email)
Term paper submission deadline: October 20, 2014
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Teaching methods
Lectures 16 h, Data Exercises 8 h
Grading
Graded 0–5 on the based on 70% exam and 10% home assignments 20% Term paper.
Prerequisites
Compulsory B.Sc. courses in Finance (except completed Bachelor’s thesis). In particular, it is
required that students have understanding of financial terms and analysis.
Timetable:
week 37 - Monday - September 08 - 13:00 - 16:00 - Room No. 7334--------- Chapter:1+2
week 37 - Wednesday - September 10 - 13:00 - 16:00 - Room No. 7334----- Chapter:2
week 38 - Monday - September 15 - 13:00 - 16:00 - Room No. 7334----------Chapter:3
week 38 - Wednesday - September 17 - 13:00 - 16:00 - Room No. 7334------Chapter:4
week 39- Monday - September 22- 13:00 - 16:00 - Room No. 7334------------Chapter:5
week 39 - Wednesday - September 24 - 13:00 - 16:00 - Room No. 7334-------Chapter:6
week 40- Monday - September 29 - 13:00 - 16:00 - Room No. 7334------------Chapter:7
week 40- Wednesday - October 01- 13:00 - 16:00 - Room No. 7334------------Chapter:8
Structure and content of academic studies
Class 1&2
Topic 1. Introduction and a brief overview of the classical linear
regression model.
Introduction
 What is econometrics?
 Types of data
 Steps involved in formulating an econometric model
 Points to consider when reading articles in empirical finance
A brief overview of the classical linear regression model
 What is a regression model?
 Regression versus correlation
 Simple regression
 The assumptions underlying the classical linear regression
model
 Properties of the OLS estimator
 Precision and standard errors
 An introduction to statistical inference
 A special type of hypothesis test: the t-ratio
Empirical data exercises with Eviews
Reading:
Brooks, Chris: Introductory econometrics for finance. Cambridge,
2002 or newer Ch. 1&2
Learning objectives and outcomes:
Understand econometrics, key steps involved in econometric modeling;
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understand the basics of univariate CLRM with interpretation of
regression results; understand the kinds of data sets that are used in
business, economics, and finance.
Home assignment:
Data Exercise 1.
Class 3
Topic 2. Further development and analysis of the classical linear
regression model.
 Generalising the simple model to multiple linear regression
 Testing multiple hypotheses: the F-test
 Data mining and the true size of the test
 Goodness of fit statistics
 components analysis
Empirical data exercises with Eviews
Reading:
Brooks, Chris: Introductory econometrics for finance. Cambridge,
2002 or newer Ch. 3
Learning objectives and outcomes:
Understand the multiple linear regression model; interpret the slopes
and constant term. Understand the use of F-test, size of the test,
Goodness of fit statistics and components analysis
Home assignment:
Data Exercise 2.
Class 4
Topic 3. CLRM assumptions and diagnostic tests
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Tests for Heteroscedasticity
Tests for autocorrelation
Multicollinearity
Testing for departures from normality
Parameter stability tests
Empirical data exercises with Eviews
Reading:
Brooks, Chris: Introductory econometrics for finance. Cambridge,
2002 or newer Ch. 4
Learning objectives and outcomes:
Understand the assumptions, violation of the assumptions and
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diagnostic tests of CLRM.
Home assignment:
Data Exercise 3.
Class 5
Topic 4. Univariate time series modelling and forecasting.
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Moving average processes
Autoregressive processes
The partial autocorrelation function
ARMA processes
Building ARMA models: the Box--Jenkins approach
Exponential smoothing
Forecasting in econometrics
Empirical data exercises with Eviews
Reading:
Brooks, Chris: Introductory econometrics for finance. Cambridge,
2002 or newer Ch. 5
Learning objectives and outcomes:
Understand the Univariate time series modelling, such as ARMA
models and forecasting using Univariate time series models.
Home assignment:
Data Exercise 4.
Class 6
Topic 5. Multivariate models.
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simultaneous equations models
exogeneity
Vector autoregressive models
Block significance and causality tests
VARs with exogenous variables
Impulse responses and variance decompositions
Empirical data exercises with Eviews
Reading:
Brooks, Chris: Introductory econometrics for finance. Cambridge,
2002 or newer Ch. 6
Learning objectives and outcomes:
Understand the Multivariate time series modelling, such as
simultaneous equations models and Vector autoregressive models
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Home assignment:
Data Exercise 5.
Class 7
Topic 6. Modelling long-run relationships in finance.
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Stationarity and unit root testing
Cointegration
Equilibrium correction or error correction models
Johansen Cointegration technique based on VARs
Empirical data exercises with Eviews
Reading:
Brooks, Chris: Introductory econometrics for finance. Cambridge,
2002 or newer Ch. 7
Learning objectives and outcomes:
Understand how to model, estimate and interpret relationships between
two variables in the long run by using different Cointegration models
Home assignment:
Data Exercise 6.
Class 8
Topic 7. Modelling volatility and correlation.
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Models for volatility
Autoregressive conditionally heteroscedastic (ARCH) models
Generalised ARCH (GARCH) models
Extensions to the basic GARCH model
Uses of GARCH-type models including volatility forecasting
Multivariate GARCH models
Empirical data exercises with Eviews
Reading:
Brooks, Chris: Introductory econometrics for finance. Cambridge,
2002 or newer Ch. 8
Learning objectives and outcomes:
Understand how to model, estimate and forecast volatility and
correlation
Home assignment:
Data Exercise 7.
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TERM PAPER GENERAL GUIDELINES: FINANCIAL ECONOMETRICS
TERM PAPER GENERAL GUIDELINES
Language of the paper:
The language of the paper is English.
Length of the paper:
The recommended length for the term paper is a cover page plus 10-15 pages including
references. Plus appendices.
Deadline for submission:
Send your final term paper in one file either in Adobe pdf or MS Word format. Dead line
for term papers is 20-10-2014. Send by email. (Kashif.saleem@lut.fi)
TERM PAPER - RESEARCH TOPICS
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1. CAPM type questions… how market risk effect of stock prices – univariate
CLRM
2. APT type questions … how different macro factors, such as Int, fx,inf,etc. effect
on stock prices – Multivariate CLRM
3. Modeling any class of assets returns…. Stock, bond, derivatives,
commodities…..ARMA process - univariate
4. Modeling any class of assets returns …. Stock, bond, derivatives,
commodities…..VAR process – Multivariate
5. Modeling long run relationship between two assets or variables (macro) ---Co
integration test
6. Modeling volatility of a single asset ….Univariate GARCH
7. Checking the leverage effect …EGARCH, GJR
8. Checking the relationship of two variables, assets volatility ---Multivariate
GARCK Models
9. Spillover effects, contagious effect, how the markets are integrated ---BEKK
model
TERM PAPER STRUCTURE
COVER PAGE
LIST OF CONTENTS
INTRODUCTION
 Describe briefly the topic of your term paper
 Story, Motivation, research question
 see, term paper questions
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LITERATURE REVIEW
 What kind of theories/theoretical / empirical framework different authors have used to
deal with the topic of your research?
 try to find mixed results in the previous published work, for example, at least - one
paper supporting expected results, one paper showing contrary to the expected results
and one paper showing mixed results, to justify your contribution.
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EMPIRICAL FRAMEWORK
 Describe briefly the methods you proposed to analyze the topic of your term paper
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For example, ARIMA family models, VAR method, Johnson Co-integration technique,
GARCH family models
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DATA
 Describe the characteristics of your data:
 graphically, Descriptive statistics ( mean, SD, Skewness, Kortosis, ADF, LM test, JB
test, etc…)
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RESULTS
 What were the main findings of the research papers?
 What are the implications of the research?
How do you think that this research could be useful to investors, portfolio managers,
companies, etc…
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CONCLUSION
 What kind of extension on the research you could imagine on this topic? .
How this term paper has increased your knowledge of the particular topic
 What are the strengths and weaknesses of the research papers? Why?
REFERENCES
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