KTTS4120 Applied Time Series Analysis for Financial Economics (5 cr)
Description
The course is an introduction to the econometric analysis of time series data, focusing especially on applications for financial economics. After a thorough recap of the standard classical linear regression model, the students will apply the tools of time series analysis to financial economic problems, rather than attempt to formally prove theoretical propositions. However, the ability to apply these techniques often requires knowledge of the underlying theory. The course begins with an overview of the nature of time series data and a review of some necessary statistical tools. The analysis of univariate and multivariate time-series models will be covered. We especially focus on stationary linear, i.e. the autoregressive-moving average (ARMA) models, non-linear models of conditional heteroscedasticity (ARCH and GARCH type models) and multivariate time series analysis in the form of vector autoregressive (VAR) models. In addition, the principal tools required for the analysis of non-stationary time series data are provided in the form of Engle-Granger and Johansen procedures.
Learning outcomes
• are able to identify single variable and multivariate models and methods for analysis of financial economic time series
• recognize the models for volatility and asset market risk
• are able to scrutinize empirically some fundamental equilibrium models for financial markets using time series data
• understand the differences in analyzing the basic models for stationary and non-stationary time series data
Additional information
Recommended timing for BIF students: 1st year.
Recommended timing for Finnish M.Sc. degree students: 3rd or 4th year.
Study materials
Lecture notes, material for demonstrations and other relevant material distributed through Moodle
Literature
- Brooks, C. 2019. Introductory Econometrics for Finance. 4th ed.; ISBN: 978-1108436823