FINANCIAL ECONOMETRICS
| Title | ΧΡΗΜΑΤΟΟΙΚΟΝΟΜΙΚΗ ΟΙΚΟΝΟΜΕΤΡΙΑ / FINANCIAL ECONOMETRICS |
| Code | 12ΥΗ05 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Emmanouil Trachanas |
| Common | Yes |
| Status | Active |
| Course ID | 600000466 |
Programme of Study: UPS School of Economics (2013-today)
Registered students: 88
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| ECONOMICS | Compulsory Course belonging to the selected specialization (Compulsory Specialization Course) | 8 | 4 | 6 |
| BUSINESS ADMINISTRATION | Elective Courses belonging to the other | 8 | 4 | 3 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 4 |
| Class ID | 600293245
|
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600293245
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
- Slide presentations
- Book
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 Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Extended Answer Questions (Summative)
- Written Exam with Problem Solving (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Brooks, C. (2022) Εισαγωγή στη Χρηματοοικονομική Οικονομετρία. Εκδόσεις Gutenberg
Additional bibliography for study
Xρήστου Γ. (2011), Εισαγωγή στην Οικονομετρία, Gutenberg.
Enders, W. (2009), Applied Econometric Analysis, John Wiley & Sons, 3rd 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
25-02-2025
