German title of this lecture:
"Ausgewählte Aspekte im Management von (Rück-) Versicherungsunternehmen"
Dr. Wolfgang Schanz (SCOR Reinsurance)
- This lecture will be held in English.
- Further information on the lecture and all documents are available on Moodle. You will get access to the course website in Moodle, once you have successfully registered for the course (see below). On the course website in Moodle, you can then also find all the information necessary for the running of the course.
Performance Requirements and Evaluation
To successfully pass this course, the following requirements need to be fulfilled by the participants/teams:
- Participation at the scheduled lessons
- Elaboration of a sub-subject along the team classification:
- Setting up of an article for each team (approx. 20 pages)
- Setting up of a presentation for each team
- Submission of an executive summary of each team
- Submission of team elaboration paper and of team presentation
- Consistent division (time wise and content wise) of the presentation for every team member (overall 45 min presentation and 15 min discussion for each team)
- During the period of editing: Participation at coordination circles for each team
The performance of the participants is evaluated in the following way:
- 50% team elaboration
- 50% individual presentation and dispute
In the summer term 2021 Dr. Wolfgang Schanz will offer a special course in reinsurance in cooperation with SCOR Reinsurance. The course will discuss the "Modelling of a parametric weather derivatives using time series analysis".
Detailed description of the topic:
"Parametric covers can be used in insurance and reinsurance to hedge against weather events, e.g., an energy provider can buy a cover which gives a payout for higher-than-average temperatures during winter in specific region to compensate for less revenue (due to less heating). Opposed to parametric covers based on cat events (e.g., earthquake), such covers are usually designed to trigger frequently (i.e., every 5 - 10 years), making a determination of the price based on historic data feasible (experience modelling). However, when exploring different options for such covers it can be of advantage to model the underlying trigger variable (e.g., daily temperature) instead of more aggregate parameters (e.g., expected annual payout). Such approach, however, introduces the question of dependencies (most likely the weather tomorrow is like the weather today). Time series analysis and models give tools to handle such questions, but there are some challenges to tackle beforehand such as the estimation of seasonality or temperature trends due to global warming. We would like the participants to explore those issues, i.e., to analyze a given time series of weather data in varying detail and its impact on the pricing of a cover based on this time series to identify issues and propose solutions and models to cope with it.
We expect from the participants some prior knowledge on time series analysis and/or the willingness to autonomously gain some, same holds for the experience with a suitable software package like R."
During the course the students will work in groups on given problems and present their solutions in the course.
The course is offered to master degree students in (Wirtschafts-)Mathematik, Wirtschaftswissenschaften and in our Master of Finance Program, who specialize in Actuarial Sciences.
Reinsurance is an important area for actuaries where many of our alumni work - several of them at SCOR Reinsurance (the 4th largest reinsurer in the world). Therefore, this course is of particular interest for every student who considers reinsurers as potential future employers.
Since the number of participants is limited you need to apply for this course by Sunday, April 25, 2021 sending an email to thorsten.sehner(at)uni-ulm.de which should include the following information:
- subject of study, number of semesters
- current transcript
- which lectures have you already heard in Actuarial Science, Financial Mathematics and Finance (including the current semester)?