# FIN 3618 Financial Econometrics

## FIN 3618 Financial Econometrics

Course code:
FIN 3618
Department:
Finance
Credits:
7.5
Course coordinator:
Isaiah Hull
Course name in Norwegian:
Financial Econometrics
Product category:
Bachelor
Portfolio:
Bachelor of Finance - Programme Courses
Semester:
2022 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 data using the programming language R (which is widely used in the financial industry) to answer questions in finance. Therefore, financial econometrics can be used to test theories in finance. As such, it supports financial decision-making.

Learning outcomes - Knowledge

During the course, students should develop knowledge about:

• The concept of regression analysis using Ordinary Least Squares (OLS).
• Statistical inference within the regression model.
• How violations of the classical assumptions underlying OLS affect the regression model.
Learning outcomes - Skills

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

• Estimate regression models using OLS.
• Perform hypothesis tests on the parameter estimates of the regression model.
• Test the assumptions underlying the classical linear regression model.

Moreover, the course provides students with the necessary skills to extract data from Wharton Research Data Services (WRDS) and other relevant data sources (e.g., Yahoo finance, FRED, or Qualcomm) and to implement the econometric techniques in the programming language R (which is widely used in the financial industry).

General Competence

In the course, the focus will be on the assumptions underlying the different theories and methods covered. Hence, it is expected that students have a critical attitude towards the realism of these.

Course content

Introduction and mathematical foundations

• Steps involved in formulating an econometric model
• Functions
• Matrices

Statistical foundations and dealing with data

• Probability and probability distributions
• Descriptive statistics
• Types of data and data aggregation
• Simple returns vs. log returns

Review: The capital asset pricing model (CAPM)

• The assumptions underlying the CAPM
• Security market line
• Capital market line
• Sharpe ratio, Treynor ratio, Jensen's alpha

The classical linear regression model (CLRM)

• Simple regression
• The assumptions underlying the CLRM
• Properties of the OLS estimator
• Standard errors
• Statistical inference
• t-statistic
• p-value

Further development and analysis of the CLRM

• From univariate to multivariate regression
• Parameter estimation and standard errors in the multivariate regression framework
• Testing multiple hypotheses: the F-test

CLRM assumptions and the diagnostic tests

• Assumption 1: Errors have zero mean
• Assumption 2: Errors have constant variance
• Assumption 3: Errors are linearly independent of each other
• Assumption 4: Errors are linearly independent of x-variables
• Assumption 5: Errors are normally distributed
• Multicollinearity
• Omitted variable bias
• Parameter stability tests
Teaching and learning activities

The course elements include lectures, in-class exercises, and two assignments. During the lectures, we will introduce new econometric techniques and discuss their practical application in R. To strengthen the students' understanding of these concepts, the students have to submit two assignments (group work). In these, they download the data themselves from a database (e.g., WRDS), import the data into R, implement the econometric analyses in R, and summarize their results in tables or graphs. Here, strong emphasis will be placed on the interpretations of the results from a statistical as well as from an economic point of view.

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 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 regarding the letter grades when the course starts.

In the case of exam components that have to be solved in groups, the course responsible decides on the allocation of the students. At re-sit, all exam components must, as a main rule, be retaken during the next scheduled course.

Software tools
R
Qualifications

Higher Education Entrance Qualification

### Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

MET 2910 Mathematics and MET 2920 Statistics or equivalent.

Assessments
Assessments
Exam category:
Submission
Form of assessment:
Written submission
Weight:
20
Grouping:
Group (1 - 4)
Duration:
1 Week(s)
Comment:
Assignment 1
Exam code:
FIN36181
Resit:
All components must, as a main rule, be retaken during next scheduled course
Exam category:
Submission
Form of assessment:
Written submission
Weight:
20
Grouping:
Group (1 - 4)
Duration:
1 Week(s)
Comment:
Assignment 2
Exam code:
FIN36181
Resit:
All components must, as a main rule, be retaken during next scheduled course
Exam category:
Submission
Form of assessment:
Written submission
Invigilation
Weight:
60
Grouping:
Individual
Support materials:
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Duration:
2 Hour(s)
Comment:
Final written examination with supervision
Exam code:
FIN36181
Resit:
All components must, as a main rule, be retaken during next scheduled course
Type of Assessment:
Continuous assessment
ECTS
Total weight:
100