| Course Name |
Financial Econometrics
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
ECON 517
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| National Occupation Classification | - | |||||
| Course Coordinator | - | |||||
| Course Lecturer(s) | - | |||||
| Assistant(s) | - | |||||
| Course Objectives | This course introduces the student to a wide range of techniques in financial econometrics, and their practical applications. Prior knowledge of statistics and econometrics is very useful, but it isn’t necessary. Each student is required to hand in a class project that applies class material to real financial data. Accordingly, one of the aims of the course is to give students the skills necessary to pursue independent research projects, and the backgrounds to be able to extend their knowledge to additional topics of interest without much difficulty. Class applications will utilize the open source econometrics software, Gretl. It can be downloaded and installed free of charge from the website: http://gretl.sourceforge.net/ |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | The course will mostly be based on Time Series econometric methods. While this is the ideal approach for an introduction to the fundamental methods of quantitative finance, the student should keep in mind that the range of econometric methods that can be used to answer questions related to finance and financial economics spans almost the entire spectrum of econometrics. The course starts by reviewing basic tools of statistics and econometrics, and makes brief introductions to regression analysis, least squares methods, and some extensions of these topics. Then, numerous time series methods are discussed, including the estimation and forecasting of ARMA and ARIMA models, models of conditional heteroscedasticity (ARCH/GARCH), vector autoregressions, and cointegration. Each topic is discussed along with its applications in finance, keeping in mind the peculiarities of financial data and methods that are designed to work with such data. |
| Related Sustainable Development Goals |
|
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses |
X
|
|
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Related Preparation |
| 1 | Foundations: A Review of Probability and Statistics | Class notes. |
| 2 | Introduction to Regression Analysis | Brooks, Chapters 2 & 3 |
| 3 | Topics in Regression Analysis | Brooks, Chapters 3 & 4 |
| 4 | Foundations of Time Series Econometrics | Brooks, Chapter 5 |
| 5 | ARMA Modeling, pt.1 | Brooks, Chapter 5 |
| 6 | ARMA Modeling, pt.2 | Brooks, Chapter 5, Class notes and additional reading material |
| 7 | Midterm Exam | |
| 8 | Nonstationarity, Unit Roots, and ARIMA Models | Brooks, Chapter 7 |
| 9 | Forecasting with Time Series | Brooks, Chapter 5 |
| 10 | Autoregressive Conditional Heteroscedasticity: ARCH and GARCH, pt. 1 | Brooks, Chapter 8, Class notes and additional reading material |
| 11 | Autoregressive Conditional Heteroscedasticity: ARCH and GARCH, pt.2 | Brooks, Chapter 8, Class notes and additional reading material |
| 12 | Stationary Vector Models: VAR | Brooks, Chapter 6 |
| 13 | Cointegration and Common Trends | Brooks, Chapter 7 |
| 14 | Additional Topic (Optional and Time Permitting) | |
| 15 | Additional Topic (Optional and Time Permitting) | |
| 16 | Review of the Semester |
| Course Notes/Textbooks | Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, Sergio M. Focardi, and Teo Jasic, Financial Econometrics: From Basics to Advanced Modeling Techniques (John Wiley & Sons, Inc.). |
| Suggested Readings/Materials | On Financial Econometrics: • Chris Brooks, Introductory Econometrics for Finance (Second Edition) • Carol Alexander, Market Models: A Guide to Financial Data Analysis. On Time Series: • Walter Enders, Applied Econometric Time Series (Second Edition) • Brockwell and Davis, Introduction to Time Series and Forecasting (Second Edition) |
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques |
-
|
-
|
| Portfolio | ||
| Homework / Assignments |
5
|
20
|
| Presentation / Jury | ||
| Project |
2
|
55
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
25
|
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade |
75
|
|
| Weighting of End-of-Semester Activities on the Final Grade |
25
|
|
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
0
|
||
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
-
|
0
|
|
| Portfolio |
0
|
||
| Homework / Assignments |
5
|
9
|
45
|
| Presentation / Jury |
0
|
||
| Project |
2
|
46
|
92
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
40
|
40
|
| Final Exam |
0
|
||
| Total |
225
|
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
|||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 |
To develop and deepen his/her knowledge on theories of mathematics and statistics and their applications in level of expertise, and to obtain unique definitions which bring innovations to the area, based on master level competencies, |
-
|
-
|
-
|
X
|
-
|
|
| 2 |
To have the ability of original, independent and critical thinking in Mathematics and Statistics and to be able to develop theoretical concepts, |
-
|
-
|
-
|
-
|
X
|
|
| 3 |
To have the ability of defining and verifying problems in Mathematics and Statistics, |
-
|
-
|
-
|
X
|
-
|
|
| 4 |
With an interdisciplinary approach, to be able to apply theoretical and applied methods of mathematics and statistics in analyzing and solving new problems and to be able to discover his/her own potentials with respect to the application, |
-
|
-
|
-
|
X
|
-
|
|
| 5 |
In nearly every fields that mathematics and statistics are used, to be able to execute, conclude and report a research, which requires expertise, independently, |
-
|
-
|
-
|
-
|
X
|
|
| 6 |
To be able to evaluate and renew his/her abilities and knowledge acquired in the field of Applied Mathematics and Statistics with critical approach, and to be able to analyze, synthesize and evaluate complex thoughts in a critical way, |
-
|
-
|
-
|
X
|
-
|
|
| 7 |
To be able to convey his/her analyses and methods in the field of Applied Mathematics and Statistics to the experts in a scientific way, |
-
|
-
|
-
|
X
|
-
|
|
| 8 |
To be able to use national and international academic resources (English) efficiently, to update his/her knowledge, to communicate with his/her native and foreign colleagues easily, to follow the literature periodically, to contribute scientific meetings held in his/her own field and other fields systematically as written, oral and visual. |
-
|
-
|
X
|
-
|
-
|
|
| 9 |
To be familiar with computer software commonly used in the fields of Applied Mathematics and Statistics and to be able to use at least two of them efficiently, |
-
|
-
|
X
|
-
|
-
|
|
| 10 |
To contribute the transformation process of his/her own society into an information society and the sustainability of this process by introducing scientific, technological, social and cultural advances in the fields of Applied Mathematics and Statistics, |
-
|
-
|
-
|
X
|
-
|
|
| 11 |
As having rich cultural background and social sensitivity with a global perspective, to be able to evaluate all processes efficiently, to be able to contribute the solutions of social, scientific, cultural and ethical problems and to support the development of these values, |
-
|
-
|
-
|
-
|
X
|
|
| 12 |
As being competent in abstract thinking, to be able to connect abstract events to concrete events and to transfer solutions, to analyze results with scientific methods by designing experiment and collecting data and to interpret them, |
-
|
-
|
-
|
X
|
-
|
|
| 13 |
To be able to produce strategies, policies and plans about systems and topics in which mathematics and statistics are used and to be able to interpret and develop results, |
-
|
-
|
-
|
-
|
X
|
|
| 14 |
To be able to evaluate, argue and analyze prominent persons, events and phenomena, which play an important role in the development and combination of the fields of Mathematics and Statistics, within the perspective of the development of other fields of science, |
-
|
-
|
-
|
X
|
-
|
|
| 15 |
In Applied Mathematics and Statistics, to be able to sustain scientific work as an individual or a group, to be effective in all phases of an independent work, to participate decision-making process and to make and execute necessary planning within an effective time schedule. |
-
|
-
|
-
|
X
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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