Course Overview

Applied Statistics

Students acquire a confident command of descriptive statistical measures and a constructive, critical approach to statements based on samples.

In this module, fundamental concepts of statistics are taught with a strong focus on applications from the field of public administration. Building on the theoretical foundations provided, students develop the ability to solve concrete applications and research questions using software-based methods.

Simulation-Based Decision Theory

Students acquire the competence to classify complex decision-making situations and to derive normative decision recommendations using appropriate optimization concepts.

The module first covers the fundamentals of decision theory (in particular decision-making under risk and under incomplete information) as well as the basics of stochastic simulation. In the second part of the course, core topics of appropriate modeling—especially of so-called subjective probabilities—are the focus. The holistic assessment of risk situations enabled by this approach, as opposed to a purely scenario-based perspective, is implemented through a wide range of software-supported practical applications.

Statistics for Economists

A sound command of statistics is one of the basic prerequisites for a successful degree in economics and organizational sciences. Statistics that are particularly relevant for economists can be broadly divided into descriptive statistics and inferential statistics, with elementary principles of probability theory being indispensable for mastering inferential statistics. The key qualification objective of the module is, in addition to the secure application of various methods (regressions, tests, etc.), above all the ability to correctly interpret statistical results. Examples include questions about the actual explanatory power of regressions (e.g., using the coefficient of determination) or of tests (the problem of asymmetry in explanatory power when accepting versus rejecting hypotheses).

Statistics I

Building on the concept of statistical variables, Statistics I covers the fundamental topics of descriptive statistics. These include, among others, key measures of distributions, multivariate regression, and the description of time series. This is followed by elementary topics in probability theory, such as probability spaces and specific probability distributions.

Statistics II

Building on Statistics I, the focus initially lies on the most important limit theorems of statistics, which ultimately form the basis of statistical test theory. Test theory is an integral part of inferential statistics, and for the various hypothesis tests covered, particular emphasis is placed on the appropriate evaluation of test decisions, especially the discussion of Type I versus Type II errors.

Project Studies

In the project studies, current issues and research questions in data analytics and statistics are addressed.

Strategic Transformation in Financial Services

Customer, market, and product dominate traditional management thinking in financial services. Owing to increasing digitalization and changing customer behavior, functional business and operating models are undergoing increasingly disruptive transformation. In addition, exogenous and endogenous shocks—such as those caused by COVID-19—may occur. What strategic responses are possible? What and whom do the current development trajectories change?

A look into the new markets of the financial sector provides initial answers and reveals significantly altered organizational forms and patterns of success (especially InsurTechs and FinTechs). These can be systematically analyzed using established theories and integrated into existing corporate practice through modern approaches to thinking and acting related to organizational ambidexterity.

An excursion to the InsurTech Hub Munich as well as a boot camp on agile working in a leading company in the financial industry deepen the academic lecture and exercise content with practical insights.

Assessment: Portfolio consisting of two one-pagers prepared for a fictional lobbying activity (addressing works councils, employees, regulators, policymakers, and society, etc.) and a group presentation to decision-makers. The examination presentation is scheduled for 15 minutes, followed by a 15-minute discussion.

Decision Analytics

Students acquire the competence to classify complex decision-making situations and to derive normative decision recommendations using appropriate optimization concepts.

They gain comprehensive theoretical knowledge for dealing with complex decision situations, with a particular focus on the appropriate modeling of individual risk preferences and the adequate consideration of ambiguity and uncertainty. Practical, software-supported exercises demonstrate the relevance of the theoretical knowledge for the responsible and rigorous application of the methods learned.

Method-Based Risk Management in Capital Markets

This module enhances methodological competence while at the same time demonstrating the applicability of concrete concepts in risk management on capital markets. Students acquire, in particular, a deep understanding of the measurement and “pricing” of risk and of the possibilities for hedging it using derivative instruments. When taken as part of the specialization “Economics and Law of the Global Economy,” this module—together with the compulsory modules and the two other elective modules—enables students to develop an integrated overall understanding of the global economy.

Two areas of risk management that have each been awarded Nobel Prizes are portfolio optimization and option pricing theory. Both areas are covered in substantial depth in this course. In addition to engaging with the necessary formal frameworks, the course places special emphasis on examining the practical implementability of these theories in everyday capital market practice, drawing on econometric methods.

Multivariate Analytics

Insurance Statistics

This module enhances methodological competence while also demonstrating the applicability of concrete concepts in insurance statistics. Students acquire, in particular, a deep understanding of the origin and design of pricing (tariff) principles in non-life insurance.

The objective of every insurance scheme is to distribute risks across a collective that is as homogeneous as possible. From the policyholder’s perspective, the premium required for inclusion in the collective must be neither too high nor too low: higher premiums reduce the economic benefit for individuals, while lower premiums increase the probability of ruin for the collective when claims occur. The course focuses on how this objective can be achieved, in particular through the use of generalized linear models (GLMs).

Master Seminar

The master seminar addresses current topics and research questions in data analytics and statistics.

General Information

The Chair of Data Analytics & Statistics offers theses at both the bachelor’s and master’s level.

Classical topic areas include, among others:

• Linear and logistic regression

• Data analysis

• Statistical tests and model quality criteria

If you wish to write a thesis with us on a specific topic, you are required to engage with the subject at the level of a scientific paper and generally to complement this by applying the relevant theory to a practical dataset.

Topics are announced on a trimester basis. If you have any questions, please contact philipp.hausenblas@unibw.de.