C24 (RT) Econometrics — University of Liechtenstein

Semester:WS 24/25

Type:Module

Language:English

ECTS-Credits:3.0

Scheduled in semester:1

Semester Hours per Week / Contact Hours:37.0 L / 28.0 h

Self-directed study time:62.0 h

Module coordination/Lecturers

Master's degree programme in Finance (01.09.2020)
Master's degree programme in Innovative Finance (01.09.2024)

This course provides a comprehensive introduction to the application of econometric techniques in finance. Stu-dents will delve into both univariate and multivariate time-series analysis, gaining insights into key concepts of modern econometrics. Overall, students will be equipped with the necessary skills to analyse and interpret com-plex financial time-series data effectively. This course combines theoretical knowledge with practical application using R. Key topics covered are:Stationarity, differencing, and co-integrationHeteroskedasticity and volatility clusteringSelf-dependence and endogeneityVector auto regressions.Time series modelling, including ARMA and GARCHImplement and empirically test the above mentioned in R

After successful completion of the course, students willProfessional competenceunderstand the complexity and pitfalls of financial data.apply advanced econometric techniques to analyse that complexity.formulate predictive models for quantitative and unbiased forecasts. - Methodological competenceformulate predictive models for financial time-series. - · understand and know how to implement models of univariate and multivariate volatility.· know how to detect, test and handle stationarity, heteroscedasticity and auto-correlation.· understand when to use univariate end multivariate time series models, know how to test and implement them and interpret the output of such models.Technological competenceconduct econometric analyses and forecast financial data using R. - Social competencesolve complex econometric problems as a team.

Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).

Project, Written Exam