5810593: C24 Statistics

Description

The purpose of this course is to familiarize students with the statistical methods and tools necessary not only for producing high quality research output in finance, but also necessary to understand and apply the quantitative tools that are at the core of a modern and innovative financial business. In this context, students will recapture common statistical concepts such as regression analysis and hypothesis testing within financial data as well as the basics of linear algebra. Simultaneously, students will learn how to use R, a statistical software that has become standard in research and industry. A third competence gained during the course is about how to find and down-load data from professional (Refinitiv) and open-source data providers.
Key topics covered are:

• Statistical programming in R (tidy data handling, programming)
• Basics of linear algebra (vectors, matrices, systems of linear equations)
• Descriptive statistics for uni- and multivariate analysis
• Time series analysis
• Hypothesis testing
• Regression analysis: uni-/ multivariate, (non-)linear

Learning Outcomes

After successful completion of the course, students will

Professional competence

• conduct empirical analysis of financial data using R.
• manage large financial datasets efficiently.
• understand and apply statistical methods to real-world financial data.

• apply simple and multiple linear regressions to financial data, including model specification, estimation, and interpretation.
• perform diagnostic tests for regression models, such as tests for heteroscedasticity, multicollinearity, and autocorrelation.
• address common issues in financial time series data.
• apply statistical methods like hypothesis testing and non-parametric tests to financial datasets.

• develop proficiency in statistical programming in R, with a focus on the tidyverse.
• efficiently manipulate and visualize data using R.
• extract and handle data from professional and open-source data providers.

Lectures Method

• The course is a combination of interactive lectures and coding sessions.
• Practical exercises and hands-on coding using R.
• Case studies and real-world data analysis projects.

Literature

• Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Appli-cations in R. Springer.
• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).