FINANCIAL ECONOMETRICS
| Title | ΧΡΗΜΑΤΟΟΙΚΟΝΟΜΙΚΗ ΟΙΚΟΝΟΜΕΤΡΙΑ / FINANCIAL ECONOMETRICS |
| Code | 12ΥΗ05 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Konstantinos Katrakylidis |
| Common | Yes |
| Status | Active |
| Course ID | 600000466 |
Programme of Study: UPS School of Economics (2013-today)
Registered students: 90
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| OIKONOMIAS | Compulsory Course belonging to the selected specialization (Compulsory Specialization Course) | 8 | 4 | 6 |
| DIOIKĪSĪ EPICΗEIRĪSEŌN | Elective Courses belonging to the other | 8 | 4 | 6 |
| Academic Year | 2023 – 2024 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 4 |
| Class ID | 600247817
|
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600247817
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=6749
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
o Acquaintance with advanced time series models and techniques.
o Good grasp of practical issues in the modeling of financial markets (non-stationarity, short-term and long-term trends, fluctuations in volatility).
o Learning popular software packages for time series analysis.
o Developing a solid understanding of practical aspects of econometric model-building (outlier detection, data visualisation, diagnostic testing, etc).
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
Course Content (Syllabus)
• The fundamentals of time-series analysis: time series vs stratified data, conditional distribution, conditional mean and variance, short-term and long-term predictions, trend, mean-reversion, periodicity.
• Popular time-series analysis techniques: autocorrelation and partial autocorrelation functions, autoregressive (AR) and moving-average (MA) models, mixed ARMA models, basic properties, model specification and diagnostics, the Box-Jenkins framework.
• Seasonal time-series analysis models: basic concepts and seasonality detection tools, extending the basic ARMA modelling framework, application in time series with strong seasonal components (product sales, power consumption, etc).
• Non-stationarity in financial time series: unit roots and non-stationarity, detecting unit roots using rules-of-thumb and formal statistical tests (DF, ADF, PP), application in the study and predictability of some key financial market indicators, co-integration and error correction models.
• Risk measuring models: types of financial risks, short-term changes in volatility levels, volatility clustering, autoregressive conditional heteroskedasticity (ARCH) and generalised autoregressive conditional heteroskedasticity (GARCH), the family of GARCH models, extensions of the basic GARCH framework - asymmetric volatility effects, application in the analysis of investment risk - estimating the Value-at-Risk of an asset.
Educational Material Types
- Notes
- Slide presentations
- Book
- Real financial markets data
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
Description
This course aims at presenting popular statistical and econometric techniques for the analysis of time-dependent financial and economic data. Students are introduced to the statistical properties of typical financial time-series, such as stock prices/returns, yield curves and foreign exchange data. Then, the focus is on teaching advanced econometric models specifically designed for this type of data. The course assumes a good level of probability, statistics and econometrics. A series of computer exercises and mini-projects helps students getting hands-on experience and a good understanding of practical issues in time series analysis.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 130 | 4.6 | ✓ | ||
| Laboratory Work | 18 | 0.6 | ✓ | ✓ | |
| Project | 20 | 0.7 | ✓ | ✓ | |
| Total | 168 | 6 |
Student Assessment
Student Assessment methods
- Written Exam with Problem Solving (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Κ. Συριόπουλος, Δ. Φίλιππας, Οικονομετρικά Υποδείγματα και Εφαρμογές, εκδ. Ε. & Δ. Ανικούλα, Θεσσαλονίκη 2010, Κωδικός Βιβλίου στον ΕΥΔΟΞΟ: 43350.
Κ. ΚΑΤΡΑΚΥΛΙΔΗΣ & Ν. ΤΑΜΠΑΚΗΣ, ΕΙΣΑΓΩΓΗ ΣΤΗΝ ΟΙΚΟΝΟΜΕΤΡΙΑ-ΑΣΚΗΣΕΙΣ, ΕΚΔΟΣΕΙΣ ΖΥΓΟΣ [8016]
Additional bibliography for study
Xρήστου Γ. (2011), Εισαγωγή στην Οικονομετρία, Gutenberg.
Enders, W. (2009), Applied Econometric Analysis, John Wiley & Sons, 3rd edition.
Brooks, Ch. (2008), Introductory Econometrics For Finance, Cambridge University Press, 2nd edition.
Box G., Jenkins, G. M., Reinsel, G. (2008), Time Series Analysis: Forecasting & Control, Prentice Hall, 4th edition.
Alexander, C. (2009), Market Risk Analysis, Four Volume Boxset, Wiley.
Last Update
09-12-2021
