FINANCIAL ECONOMETRICS

Course Information
TitleΧΡΗΜΑΤΟΟΙΚΟΝΟΜΙΚΗ ΟΙΚΟΝΟΜΕΤΡΙΑ / FINANCIAL ECONOMETRICS
Code12ΥΗ05
FacultyEconomic and Political Sciences
SchoolEconomics
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CoordinatorKonstantinos Katrakylidis
CommonYes
StatusActive
Course ID600000466

Programme of Study: UPS School of Economics (2013-today)

Registered students: 107
OrientationAttendance TypeSemesterYearECTS
OIKONOMIASCompulsory Course belonging to the selected specialization (Compulsory Specialization Course)846
DIOIKĪSĪ EPICΗEIRĪSEŌNElective Courses846

Class Information
Academic Year2020 – 2021
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600178433
Course Type 2016-2020
  • Scientific Area
  • Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Distance learning
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
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures1304.6
Laboratory Work180.6
Project200.7
Total1686
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
24-02-2022