# FIN 3618 Financial Econometrics

## FIN 3618 Financial Econometrics

Course code:
FIN 3618
Department:
Finance
Credits:
7.5
Course coordinator:
Patrick Konermann
Course name in Norwegian:
Finansiell økonometri
Product category:
Bachelor
Portfolio:
Bachelor of Finance - Programme Courses
Semester:
2017 Autumn
Active status:
Active
Level of study:
Bachelor
Teaching language:
English
Course type:
One semester
Introduction

Financial Econometrics can be understood as the application of statistical techniques to answer questions in finance. Financial Econometrics can therefore be used to test theories in finance. As such, it is supports financial decision-making.

Learning outcomes - Knowledge

During the course students should develop knowledge of:

• The role of Financial Econometrics in financial research.
• The steps involved in formulating, estimating and evaluating an econometric model.
• The role of descriptive statistics.
• The difference between correlation and regression.
• The concept of regression analysis using Ordinary Least Squares (OLS).
• Statistical inference within the regression model.
• How violations of the classical assumptions under lying OLS affect the regression model.
• Different model mis-specifications and biases.
• The concept of parameter stability/structural breaks.
• Univariate time series models (ARMA models).
• Forecasting financial variables.
• The concept of simultaneity bias.
• The concept of exogeneity.
• The estimation procedures for simultaneous equations systems.
• Vector autoregressive (VAR) models.
• Impulse responses and variance decompositions.
• The concept of stationarity.
• The concept of cointegration.
• Equilibrium or error correction models.

Learning outcomes - Skills

Upon completion of the course the students should be able to:

• Estimate and interpret descriptive statistics for the variables used in the model of interest.
• Estimate regression models using OLS.
• Make statistical inference (Hypothesis testing and/or Confidence Intervals) on the parameter estimates of the model.
• Perform the various tests of the classical assumptions underlying OLS.
• Identify potential mis-specifications and biases.
• Perform parameter stability tests (test for structural breaks).
• Use Information Criterias in order to select the appropriate Univariate time series model (ARMA model).
• Estimate and interpret Univariate time series models (ARMA models).
• Use forecasting techniques in order to forecast different financial variables.
• Use different evaluation criteria for forecast precision.
• Perform tests for exogeneity.
• Apply appropriate estimation techniques for simultaneous equation systems (ILS, 2SLS).
• Estimate vector autoregressive (VAR) models.
• Estimate impulse responses and variance decompositions.
• Perform tests for stationarity (Unit root tests).
• Perform tests for cointegration.
• Estimate equilibrium or error correction models.

Learning Outcome - Reflection
• In the course there will be focus on the assumptions underlying the different theories and methods covered. Hence, it is expected that students will have a critical attitude towards the realism of these. The students should upon completion of the course have a good understanding of the practical applicability of the theories and methods covered.
Course content

Introduction

• What is Financial Econometrics about
• Types of financial data
• Returns in financial modelling
• Steps involved in formulating an econometric model

Mathematical and statistical foundations

• Functions
• Matrices
• Probability and probability distributions
• Descriptive statistics

The classical linear regression model

• Regression versus correlation
• Simple regression
• The assumptions underlying the classical linear regression model
• Properties of the OLS etsimator
• Precision and standard errors
• Statistical inference

Further development and analysis of the classical linear regression model

• Multiple regression
• Testing multiple hypothesis: the F-test
• Goodness of fit statistics

Classical linear regression model assumptions and the diagnostic tests

• Heteroscedasticity
• Autocorrelation
• Non-stochastic explanatory variables
• Multicolinearity
• Specification mistakes and biases
• Parameter stability tests

Univariate time series modelling and forcasting

• Moving average (MA) processes
• Autoregressive (AR) processes
• The Box-Jenkins methodology (Information criteria)
• Forecasting financial variables

Multivariate models

• Simultaneous equations bias
• Exogeneity
• Estimation procedures for simultaneous equations systems
• Vector autoregressive (VAR) models
• Impulse responses and variance decompositions

Modellling long-run relationships in finance

• Stationarity and unit root testing
• Cointegration
• Equilibrium correction or error correction models
• Methods of parameter estimation in cointegrated systems
Learning process and requirements to students

A class will typically consist of a review of the last class, a lecture introducing new material and exercises that are solved by the lecturer or students. Each main topic will be accompanied by a hands-on practical application of an empirical finance topic. The software package EViews will be an integral part of the coursework.

If a student misses a class, it is her/his responsibility to obtain any information provided in class that is not included on the course homepage/itslearning or in the text book.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course start.

At re-sit all exam components must, as a main rule, be retaken during next scheduled course.

Software tools
EViews
Matlab

.

Qualifications

Higher Education Entrance Qualification.

Required prerequisite knowledge

MET 2910 Mathematics and MET 2920 Statistics or equivalent.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
FIN36181
Point scale
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No 1 Week(s)Group/Individual (1 - 5)
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
FIN36181
Point scale
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No 1 Week(s)Group/Individual (1 - 5)
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
FIN36181
Point scale
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
60Yes3 Hour(s)
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Group/Individual (1 - 5)
Exams:
 Exam category: Submission Form of assessment: Written submission Weight: 20 Invigilation: No Grouping (size): Group/Individual (1-5) Support materials: Duration: 1 Week(s) Comment: Exam code: FIN36181 Grading scale: Point scale Resit: All components must, as a main rule, be retaken during next scheduled course
 Exam category: Submission Form of assessment: Written submission Weight: 20 Invigilation: No Grouping (size): Group/Individual (1-5) Support materials: Duration: 1 Week(s) Comment: Exam code: FIN36181 Grading scale: Point scale Resit: All components must, as a main rule, be retaken during next scheduled course
 Exam category: Submission Form of assessment: Written submission Weight: 60 Invigilation: Yes Grouping (size): Group/Individual (1-5) Support materials: BI-approved exam calculator Simple calculator Bilingual dictionary Duration: 3 Hour(s) Comment: Exam code: FIN36181 Grading scale: Point scale Resit: All components must, as a main rule, be retaken during next scheduled course
Exam organisation:
Continuous assessment
ECTS
Total weight:
100