This module will introduce the students to time series analysis, financial econometrics and their applications. The course consists of three parts:
Part I - Introduction to time series analysis;
Part II: Introduction to financial econometrics;
Part III: Introduction to multivariate time series.
Main topics of Part I are: basic concepts of time series, weak and strong stationarity, well known operators, AR (autoregressive), MA (moving average), ARMA, ARIMA (autoregressive integrated moving average) and RW (random walk) processes, properties of those processes, estimation, model selection and forecasting using the selected model, additive model for time series with trend and seasonality, smoothing of such time series.
Part II deals with the following topics: properties of financial time series, ARCH (autoregressive conditional heteroskedasticity), GARCH (generalized ARCH), estimation and application of GARCH, VaR (value at risk) and CVaR (conditional VaR), different extensions of GARCH, ACD (autoregressive conditional duration) for modeling high-frequency data, semiparametrisc GARCH models with trend in volatility.
In Part III VAR (vector AR) processes and MGARCH (multivariate GARCH) models will be introduced briefly.
Further information can be found in the Modulhandbuch