Elective Course - 4th Semester (Spring 2nd year) | Course ID: Q210 | E-Class
According to the curriculum, students must choose three elective courses during the fourth semester

Lecturers

Dimitra Kyriakopoulou

Simos Meintanis

Language of instruction

Greek

Course content

Statistical and Econometric Models is an advanced course for students who have already acquired the basic knowledge in inferential statistics, econometrics, mathematics, and financial theory. The main purpose of this course is to enable students to understand the usefulness of financial econometrics in the analysis of relevant theory through the evaluation of specialized time series models, which are GARCH models and their extensions, the assessment of their validity and the making of econometric forecasts. These models, although useful in economics, are not sufficiently studied in the basić courses in Statistics and Econometrics. The approach to the syllabus emphasizes the foundation and understanding of the relevant techniques in GARCH, and where possible, their application to economic data using a computer.

Lectures

1. Introduction to time series and related concepts
2. Introduction and properties of the ARCH conditional heteroskedasticity model
3. Examples - applications in R and E-Views
4. Variants of the ARCH model, the generalized GARCH model and its properties
5. Univariate time series modelling methodologies (AR, MA) and their properties, Box-Jenkins methodology (ARMA models)
6. Examples - applications in R and E-Views
7. Evaluation of GARCH models
8. Practical applications of GARCH models and their use for predictions
9. Examples - applications in R and E-Views
10. Specialized GARCH-type models with wide use in financial theory and introduction to multivariate GARCH models
11. Special topics in risk management and econometric techniques and applications
12. Examples - applications in R and E-Views
13. Repetitive Exercises and Review of the Material

Bibliography

• Francq, Christian, and Jean-Michel Zakoian (2010) GARCH Models: Structure, Statistical Inference and Financial Applications, Chichester: Wiley.
• Chernick, Michael R. (1999): Bootstrap Methods: A Practitioner's Guide, New York, John Wiley and Sons.

Assessment

The course is assessed by written examination at the end of the semester. The examination includes problem-solving questions. Students are assessed on their understanding of key concepts and critical thinking.