EXC 3672 Analyses of Financial Data
EXC 3672 Analyses of Financial Data
This course covers important econometric techniques that are used in empirical finance. The main focus will be on time series econometrics as time series is the most frequent data format in finance. The course starts by examining basic regression analysis. Then it continues on to forecasting and the modelling of long-run relationships in finance, before it finally focuses on the modelling of volatilty.
The aims of this course are to:
- Introduce students to important empirical quantitative techniques used in finance and more generally in business and economics.
- Make students able to apply them appropriately.
- Prepare students for subsequent course work in finance, business, and economics.
More specifically, on completion of the course the students acquired knowledge and skills should be as follows:
- Understand in depth what is meant by correlation and regression analysis - and the difference between them
- Understand in depth what is meant by Ordinary Least Squares (OLS) - the estimation technique used in order to estimate our econometric model.
- Understand in depth the limits and assumptions of regression analysis and the consequences that follows from violations of the different assumptions.
- Understand the rationale for dummy variables; what they are and how they can be used.
- Understand the rationale for univariate time series and how these can be for forecasting economic variables.
- Understand how long-run relationships in finance can be modelled. More specifically; understand the concepts of Stationarity, Cointegration and Error Correction Models.
- Understand how the volatility of economic variables can be modelled. More specifically; understand how ARCH-type of models work.
- Understand how the programming language R works and be familiar with basic R syntax.
On completion of the course students should be able to use software like R in order to:
- Perform basic data handling
- Estimate financial models formulated as linear regression models
- Test the statistical assumptions underlying OLS.
- Estimate regression models with dummy variables.
- Estimate appropriate univariate time series models and use them to forecast economic variables, and then evaluate the quality of the forecast.
- Test for Stationarity and Cointegration
- Estimate Error Correction Models.
- Estimate appropriate volatility models (ARCH-type models)
Students will acquire a conscious and critical attitude towards different data types and their handling, towards econometric analysis, and towards the assesment and interpretation of results from empirical research.
Students will also by completing the course develop both their analytical and programming skills.
This course introduces students to empirical techniques that are relevant for finance and business and economics in general. More specifically, the outline of the course is as follows:
Foundations for empirical methods in finance.
- What is econometrics?
- Regression analysis with Ordinary Least Squares (OLS)
- Introduction to R
- Regression with Dummy variables
- Univariate time series models and forecasting
- Modelling long-run relationships in finance
- Modelling volatility
Each topic will be accompanied by a hands-on practical application of an empirical finance topic.
The software package R will be an integral part of the coursework. R is a software program that has become a standard for data analysis inside academia and corporations, especially in the finance industry. It is an open source software available free of charge on internet. The use of R will introduce students to some of the basics of programming. Programming is a skill typically required in the financial industry.
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.
Computer-based tools: Google, Yahoo finance, Quandl, and itslearning.
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.
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The specialisation requires two years of university education in Business Administration or equivalent.
EXC 2910 Mathematics, EXC 2904 Statistics and EXC 3506 Research Methods and Econometrics. Or equivalent.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 10 Grouping: Group/Individual (1 - 4) Duration: 1 Week(s) Comment: R-assignment. Exam code: EXC36721 Grading scale: Point scale leading to ECTS letter grade 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/Individual (1 - 4) Duration: 2 Week(s) Comment: Home assignment Exam code: EXC36721 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 70 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: EXC36721 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 39 Hour(s) | |
Other in classroom | 3 Hour(s) | Computer sessions. |
Student's own work with learning resources | 146 Hour(s) | Review of the slides every evening after the lecture. |
Group work / Assignments | 12 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.