• Wallmeier Martin

• Chardonnens Patrick

The goal of this course is to bridge the gap between understanding financial theories, models and concepts and implementing them using the R software of statistical computing. In the first part of the course, students will learn how to download financial data from Refinitiv Datastream and other sources, how to import data into R and how to create a tidy database. This part will also introduce important structural elements of R programs, such as functions and group-wise transformations. In the second part of the course, students will work on their own empirical project using R. The data will be individual to each student, but similar topics will be assigned to groups of students to allow and encourage cooperation. At the end of the course, students should be able to independently implement a project in R, e.g., as part of a master's thesis or later in practice.

Preliminary list of topics

• Stylized facts of stock returns and calibration of GARCH models
• Simulation of investment strategies and return distributions (e.g. momentum strategies; delta-hedged returns; volatility selling; portfolio insurance strategies)
• Valuation of options and structured products with Monte Carlo Simulation
• Forecasting of correlation matrices
• Volatility forecasting
• Stability of betas and beta forecasting
• Fama-MacBeth two-step asset pricing tests (for potential pricing factors such as skewness, kurtosis or sustainability rating)
• Evolution of financial ratios of sorted portfolios
• Forecasting ROE / ROA (with cross-sectional and panel methods)

No prior knowledge of R is required for the course, although some programming experience and knowledge of R is beneficial.

Evaluation

The evaluation will be based on the individual project (written presentation and code, 75%) and common group presentations (25%).

Limited number of participants

The number of students in this course is limited to 20. More advanced students are given priority. More information will be given in the first lecture.

Wickham, H. & Grolemund, G., R for Data Science, O’Reilly 2017.

Grolemund, G., Hands-On Programming with R, O’Reilly 2014.

Brooks, C., Introductory Econometrics for Finance, 4^th ed., Cambridge University Press 2019

Wiederholungsmöglichkeit nur für die mündliche Prüfung. Eine Wiederholung der Projektarbeit, die im Rahmen dieses Seminars zu erstellen ist, ist faktisch nicht möglich.

Course with continuous evaluation: after the registration period, you can no longer cancel your registration (see session calendar on the Faculty's website).