Semester:WS 16/17
Art:Vorlesung
Plansemester:1
Lektionen / Semester:28.0 L / 21.0 h
Selbststudium:69.0 h
Art:Vorlesung
Plansemester:1
Lektionen / Semester:28.0 L / 21.0 h
Selbststudium:69.0 h
Modulleitung/Dozierende
- Florian Schaller, MSc
(Modulleitung)
- Ass.-Prof. Dr. Sebastian Stöckl
(Interner Dozent)
Studiengang
Masterstudium Finance (01.09.2015)Module
Beschreibung
This course will cover:
- Multivariate time series models
- Long-run relationships in finance
- Models of time series volatility and covariances
Lernergebnisse
Students...
- understand multivariate time series models (Vector Autoregression - VAR)
- can estimate VAR-models and produce joint forecasts
- are able to conduct statistical inference in VAR-models
- understand the concept of stationarity and cointegration in multiple timeseries models
- comprehend models of equilibrium and error correction
- can estimate models of equilibrium and error correction
- are able to conduct statistical inference in cointegrated systems
- understand different models for volatility, such as the basic ARCH/GARCH model and extensions thereof
- comprehend models of covariances and correlation
- can estimate such models of volatility and covariances
- can apply all these models to practical problems in finance
Kompetenzen
Voraussetzungen (inhaltlich)
Students should have a working understanding of single time series models of the ARIMA-type, both in a theoretical manner as well as regarding its practical implementations (estimation and statistical inference) in R or EViews (or any other appropriate statistical programme)
Literatur
Brooks, C. (2014). Introductory econometrics for finance (3rd ed.). Cambridge: Cambridge University Press
Prüfungsmodalitäten
Written exam (60 minutes)
Termine
Datum | Zeit | Raum |
06.10.2016 | 13:15 - 16:30 | H4 |
20.10.2016 | 13:15 - 16:30 | H4 |
27.10.2016 | 13:15 - 16:30 | H4 |
10.11.2016 | 13:15 - 16:30 | H4 |
22.11.2016 | 09:00 - 12:15 | H4 |
29.11.2016 | 13:15 - 16:30 | H4 |
01.12.2016 | 13:15 - 16:30 | H4 |
Prüfungen
- PWW-MA_Econometrics (WS 16/17, bewertet)
- PWW-MA_Econometrics (SS 17, bewertet)