4408079: Econometrics

Description

This course will cover:

• Multivariate time series models
• Long-run relationships in finance
• Models of time series volatility and covariances

Learning Outcomes

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

Admission Requirements

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)

Literature

Brooks, C. (2014). Introductory econometrics for finance (3rd ed.). Cambridge: Cambridge University Press