Financial and Spatial Econometrics
- Credits: 10
- Ending: Examination
- Range: 16sP
- Semester: summer
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Upon successful completion of the course, students will acquire the following knowledge:
- knowledge of the possibilities of modeling the volatility of financial time series as well as knowledge of econometric techniques for modeling data with respect to their location in geographical space.
Upon successful completion of the course, students will acquire the following skills:
- ability to use advanced techniques of financial and spatial econometrics,
- practical skills associated with the use of selected econometric software, such as R software and GeoDa.
Upon successful completion of the course, students will acquire the following competencies:
- competencies associated with the use of models and methods of financial and spatial econometrics in solving specific financial / economic problems.
Indicative content
The aim of teaching the subject in the third level of study is to provide extended knowledge about the possibilities of using financial and spatial econometric approaches in the analysis of economic processes using econometric software R and GeoDa software.
1. Volatility models. Autoregressive conditional heteroskedasticity and stochastic volatility. One-dimensional and multidimensional models.
2. Spatial econometric models for cross-sectional and panel data, spatial autocorrelation, spatial heterogeneity.
3. Application of financial and spatial econometric instruments in the analysis of the linkages among financial markets or among different spatial units. Analysis of "spillover" effects.
Support literature
1. BAUWENS, L., HAFNER, C., LAURENTET, S. 2012. Handbook of Volatility Models and Their Applications. New Jersey: John Wiley & Sons.
2. WANG, P. 2009. Financial Econometrics. New York: Routledge.
3. CHOCHOLATÁ, M. 2016. Different approaches to stock market linkages : evidence from CEE-3 countries. In Advances in Applied Business Research: the L.A.B.S. initiative. New York: Nova Science Publishers, 49-70.
4. CHOCHOLATÁ, M. - FURKOVÁ, A. 2017. Does the location and institutional background matter in convergence modelling of the EU regions? Central European Journal of Operations Research, 25(3), 679-697.
5. ARDIA, D., BLUTEAU, K., BOUDT, K., CATANIA, L. 2018. Forecasting risk with Markov-switching GARCH models: A large-scale performance study. International Journal of Forecasting, 34 (4), 733–747.
6. ANSELIN, L., REY, S. J. 2014. Modern Spatial Econometrics in Practice. Chicago: GeoDa Press LLC.
7. CHI, G., ZHU, J. 2019. Spatial Regression Models for the Social Sciences. Thousand Oaks, CA: SAGE Publications.
8. ELHORST, J. P. 2014. Spatial Econometrics. From Cross-Sectional Data to Spatial Panels. Heidelberg: Springer-Verlag.
9. GENIAUX, G., MARTINETTI, D. 2018. A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Regional Science and Urban Economics, 72, 74–85.
Requirements to complete the course
Writing of projects
Combined final exam
Student workload
10 credits x 26 hours = 260 hours
Distribution of study load
260 hours
16 hours participation in consultations
44 hours preparation for consultations
100 hours of project processing
100 hours exam preparation
Language whose command is required to complete the course
Slovak, English
Date of approval: 10.02.2023
Date of the latest change: 16.05.2022