Financial Econometrics and Geoinformatics in Social Sciences using R 2400-ZEWW952

The main aim of the course is to make students familiar with the broad variety of data science methods for financial and spatial data, using geoinformatics methods and financial econometrics.

Course is divided into two parts. The first one will be conducted by visiting scholar: prof. Josip Arnerić and the other will be conducted by another visitng scholar: prof. Dimitris Ballas.

The course will be coordinated by an onsite lecturer – mgr Maria Kubara, while the whole class material will be delivered by the visiting professors.

The course will be taught in an intensive workshop setting over the course of two weeks between 12 and 23 May 2025. The students are asked to bring their own laptops with R v.3.3.0+ and RStudio Desktop installed in order to take active part in the practical live code exercises discussed during the class
Day 1 - Financial time-series and asset-pricing
Loading financial time-series from public sources and financial platforms into R. Preparation and transformation of raw data. Properties of financial time-series. Volatility clustering. Heavy tails phenomena. Asymmetry of information. Heteroscedasticity. Long memory. Price jumps. Microstructural noise. Fitting a return distributions and examination of their characteristics. A single-factor and multi-factor asset pricing models are considered (CAPM, ICAPM, APT and their variations), as well as forecasting models ARIMA, TAR, SETAR and regime switching models. Demonstrated financial applications in R using actual data.
Day 2 - Univariate GARCH models
Estimating and measuring volatility (realized, implied and integrated variance approaches). Univariate GARCH models. ARCH effects. Leverage effect, cross-correlation and sign bias test. Symmetric GARCH models. Asymmetric GARCH models. Lag selection criteria. News impact curve. Long-run volatility. Volatility persistence. Estimation method and algorithms assuming different distributions of innovations. Stability conditions. Goodness-of-fit, diagnostic checking and forecasting. Selected non-linear volatility models. FIGARCH model, GARCH model with regime switching and neural network GARCH. Demonstrated financial applications in R using actual data.
Day 3 Multivariate GARCH models
Multivariate GARCH models. Time varying covariance matrix. Direct generalization of univariate GARCH models (VEC, DVEC, BEKK). Non-linear combination of univariate GARCH models (CCC, DCC). Quasi-maximum likelihood estimation method and algorithms. Diagnostic checking and forecasting. MGARCH vs panel GARCH vs realized covariance. Demonstrated financial applications in R using actual data (examining time-varying betas and safe-haven properties of various assets).
Day 4: Realized volatility and realized co-volatility
Nonparametric volatility estimators. HL estimators. OHLC estimators. Financial properties of high-frequency data. Daily patterns. Data cleaning. Sampling frequency selection. Realized variance and realized covariance. Realized range estimator. Signature plot. Estimators of integrated variance which are robust and non-robust to microstructure noise and/or price jumps (RCOV, BPCOV, ROWCOV, RTCOV, TTSCOV, and others). Different synchronizations schemes. HAR model. HAR-GARCH model. Heavy model. Demonstrated financial applications in R using actual data.
Day 5: Risk measures and extreme value theory
Different approaches to estimating Value-at-Risk and Expected Shortfall, the two most common risk measures in practice, are discussed (RiskMetrics, GARCH, and quantile-based methods, among others). Pros and cons of parametric and non-parametric estimation techniques are summarized. Theory of extreme values. Modelling extreme values above the threshold. Generalized distribution of extreme values (GEV and GPD). Additionally, methods for estimating parameters of heavy-tailed distributions are examined. Demonstrated financial applications in R using actual data.

The second part of the course:
(19-23 May, classes with the second visiting scholar)

Instructor:
Dimitris Ballas
Full Professor (Chair in Economic Geography), University of Groningen
https://www.rug.nl/staff/d.ballas/cv

Course outline:
Geographical Information Systems (GIS) and Geoinformatics provide increasingly important tools in the social sciences and especially in the development and monitoring of social and economic policy. This course will introduce students to some of the key data sources used for this type of analysis. It will also introduce students to a range of techniques used for the mapping (including human cartography) and analysis of socio-economic data and for the combination of spatial and non-spatial information. Some of the practical and policy-related issues which arise in this type of analysis are also considered. The module will introduce students to a wide range of GIS applications in the Social Sciences (with a particular methodological focus on social geosimulation methods), including socio-economic impact assessment, socio-spatial policy analysis, retail demand and supply modeling and the new emerging field of happiness economics and the spatial economics of happiness. The course includes practical sessions using state-of-the-art software, including spatial microsimulation code in R.

Course objectives and learning outcomes:
• To introduce students to the techniques and issues related to the use of Geographic Information Systems in the social sciences
•To introduce students to the use of GIS and Geoinformatics for socio-economic policy analysis
• To enable students to apply a variety of GIS and related spatial modelling methods and techniques to socio-economic geographic data
•Provide students with an understanding of the ways in which spatial data can be visualised in order to aid understanding of patterns of social and spatial inequality.
•To enable students to carry out independent research in the area of GIS and socio-economic applications

By the end of the course, a student will be able to demonstrate:
• a good understanding of the socio-economic data and methods available to geovisualise and analyse public policies.
• an understanding of the geographical implications of urban, regional and national social policies.
• an understanding of the practical and ethical problems associated with the use of GIS and socio-economic data sets.
• An awareness of the importance of geographical dimensions of public policy.

Indicative course schedule (five 3-hour sessions):
1) The role of GIS and Geoinformatics in the Social Sciences
2) Geographical Scale and Human Geographical Enquiry: Alternative Human-Scaled Visualizations; creating human cartograms
3) Classifying areas and people; indexes of deprivation; geodemographic classifications
4) Geosimulation in the Social Sciences; microsimulation and spatial microsimulation; agent-based modelling
5) Applications in relation to the geographies of happiness and discontent; mapping and analysing income, well-being and happiness

Indicative reading list:
Ballas, D, Clarke, G P, Franklin, R S, Newing A (2017), GIS and the Social Sciences: Theory and Applications, Routledge
Ballas, D, Rossiter, D, Thomas, B, Clarke, G.P, Dorling, D (2005), Geography matters: simulating the local impacts of national social policies, Joseph Rowntree Foundation contemporary research issues, Joseph Rowntree Foundation, York
Ballas, D, Clarke, G. P. and Dewhurst, J (2006), Modelling the socio-economic impacts of major job loss or gain at the local level: a spatial microsimulation framework, Spatial Economic Analysis, vol. 1(1), pp. 127-146.
Ballas, D., Dorling, D. and Hennig, B.D. (2017) Analysing the regional geography of poverty, austerity and inequality in Europe: a human cartographic perspective, Regional Studies, vol. 51, pp. 174-185
Ballas, D, Tranmer M (2012), Happy People or Happy Places? A Multi-Level Modelling Approach to the Analysis of Happiness and Well-Being, International Regional Science Review, vol. 35, 70-102.
Brereton, F., Clinch, J. P. & Ferreira, S. (2008), Happiness, geography and the environment, Ecological Economics, vol. 65, pp. 386-396
Broomhead, T, Ballas, D, Baker, S (2023), Oral health, sugary drink consumption and the soft drink industry levy: using spatial microsimulation to understand tooth decay, Regional Science, Policy and Practice, https://rsaiconnect.onlinelibrary.wiley.com/doi/abs/10.1111/rsp3.12682
Campbell, M, Ballas, D (2013), A spatial microsimulation approach to economic policy analysis in Scotland, Regional Science Policy and Practice, volume 5, pp. 263–288.
Koeppen L, Ballas D, Edzes A, Koster S (2021) Places that don’t matter or people that don’t matter? A multilevel modelling approach to the analysis of geographies of discontent, Regional Science Policy and Practice, volume 13, pp. 221-245 https://doi.org/10.1111/rsp3.12384
Layard, R (2010), Measuring Subjective Well-Being, Science, vol 327, pp. 534-535.
Lovelace, R, Ballas, D (2013), `Truncate, replicate, sample': A method for creating integer weights for spatial microsimulation, Computers, Environment and Urban Systems, volume 41, pp. 1-11.
Oswald, A and Wu S (2010), ‘‘Objective Confirmation of Subjective Measures of Human Well-Being: Evidence from the U.S.A.’’ Science 327: 576–579.
Panori, A, Ballas, D, Psycharis, Y (2017), SimAthens: A spatial microsimulation approach to the estimation and analysis of small-area income distributions and poverty rates in Athens, Greece, Computers, Environment and Urban Systems, vol 63, pp. 15-25
Ziogas, T., Ballas, D., Koster, S., Edzes, A (2023), Happiness, Space and Place: Community Area Clustering and Spillovers of Life Satisfaction in Canada. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-023-10203-x

Online resources:
GIS and the Social Sciences – e-resources
https://www.routledge.com/GIS-and-the-Social-Sciences-Theory-and-Applications/Ballas-Clarke-Franklin-Newing/p/book/9781138785120

MOBI-TWIN project: https://mobi-twin-project.eu/
PHOENIX project: https://phoenix-horizon.eu/

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Both parts of the course will be complementary and will provide the students with a broad overview of the financial and geographical data analysis and their applications in R.