BE-510 Empirical Finance

The course is connected to the following study programs

• Master's Programme in Business Administration
• Master's Programme in Business Administration (5 years)

Teaching language

English.

Prerequisites

Basic econometrics, statistical programming language R, basic theories of finance

Course contents

The course tries to deepen and extend the students’ knowledge of econometrics for finance and to upgrade the students’ ability to apply related techniques to complex real life data sets using R. Moreover, we aim at improving the students’ ability to relate findings to a wider economic context and present results in an adequate form. 

The already existing portfolio of econometric models for multivariate financial time series is refined and extended. For instance, the concepts like the impulse response function and variance decomposition for VAR’s will be elaborated further. Factor analytic techniques suitable for financial data will be introduced and applied. The rationale underlying the multivariate GARCH model will be explored. The estimation of the model as well as the interpretation of parameter estimates will be practiced. Moreover, the idea of Kalman filtering will be introduced. The potential of the concept for financial data analysis will be explored. In many financial markets several equilibria coexist (bull and bear markets). To facilitate the analysis of data from such markets we introduce basic regime switching models. Finally, we extend the toolbox for the analysis of panel data to enable student to implement event studies in the financial context.

Moreover, the course focuses on the relationship prevailing between the finance sector and the macroeconomy. Knowledge concerning this relationship is primarily generated through the application of advanced econometric techniques to complex financial and macroeconomic data. Although playing a ‘’guiding’’ role, macroeconomic theory is largely implicit. The topics addressed include: The role and efficacy of central bank interventions, relationships between key macroeconomic- and financial variables, the relationship between bond- and stock yields, the empirical analysis of bulls and bear markets, and intermarket analyses. In addition, there is room for the inclusion of current issues. Central aspects of these topics will be covered during lectures. Special advice will be given concerning the econometric tools suitable for an empirical approach to the problem at hand.

Learning outcomes

Upon successful completion of this course the student is able to

• apply advanced econometric methods for finance and discuss their merits and limitations

• analyze how the financial sector is related to the wider macro economy

• structure a given research problem using ideas from finance, statistics and mathematics

• independently design and implement an analytical approach to learning from financial data

• apply complex model-based data analytical techniques to a broad range of financial data using adequate algorithms coded in R

• estimate advanced, complex financial models, test hypotheses and generate responsible forecasts using R

• to write statistical and/or financial research reports

• effectively communicate research results to specialists and non-specialists in oral and written form

Examination requirements

Course participation and presentations of project results and research paper (term paper). More information is available in Canvas.

Teaching methods

The course consists of lectures and group work sessions.

The empirical analysis itself will be subject of the weekly student (group) projects. Each week, the groups will present findings in short technical reports and oral presentations.

Expected total workload: 200 hours.

We use a dual approach to conveying knowledge about the role of the financial sector in an economy. While theoretical/institutional and other aspects of a given relationship are discussed during the lecture, the student learns about the real salient relationship via econometric work and data analysis from real life complex financial and macroeconomic data. Although guided by the lecturer, the student groups work largely independently on their projects.

Evaluation

End of term evaluation

Assessment methods and criteria

Individual research paper (100 %). Grading by letters.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course	Reduction of Credits
SE-506 – Advanced Econometrics for Finance	2.5