Special Aspects of Insurance Economics


Dr.  Peter Hieber


Masterseminar 2/0 SWS (4 ECTS)


This seminar takes place as a block seminar. The attendance at all seminar dates is required.


In this seminar, we are going to focus on some topics in actuarial science including life and non-life insurance. We are specifically dealing with how data analytics is used to design a better insurance contract. Further, we tackle different types of risk inherent in a life insurance contract and optimal retirement products. The seminar is based on scientific papers that summarize recent results in this area.

Target group

The seminar is suitable for Master students in Mathematik, Wirtschaftsmathematik, Wirtschaftswissenschaften or Finance. Previous knowledge in Life, Health, and Pension Mathematics, Insurance Economics and Financial Mathematics 1 are helpful.

Seminar performance

Typically, seminar papers are distributed to a group of 2 students.
The seminar performance consists of three parts:

  •  A seminar presentation about a selected topic. The presentation typically includes some
    theoretical derivations / model introduction and some numerical part that applies the
    esults in a realistic setup.
    Duration of the presentation: 90 minutes (including discussion).
  • A written formulation of the presentation documents as a support for the participants of
    a maximum length of two pages.
    Delivery of the presentation documents: at least one week before the presentation via email
    to peter.hieber@uni-ulm.de. The presentation documents are created jointly.
  • Active participation in this seminar.

Based on the performance, every participant will be credited with an (internal) grade.

Seminar Papers

  1. Chen, A., Rach, M., & Sehner, T. (2020). On the optimal combination of annuities and
    . ASTIN Bulletin, 50(1), 95-129.
  2. Chen, A., Hieber, P. & Klein, J. (2019). Tonuity: a novel individual-oriented
    retirement plan
    . ASTIN Bulletin, 49(1), 5-30.
  3. Denuit, M. (2020): Investing in your own and peers’ risks: the simple analytics of P2P
    , European Actuarial Journal 10(2).
  4. Eckert, J. and Gatzert, N. (2018): Risk- and Value-Based Management for Non-Life
    Insurers under Solvency Constraints
    , European Journal of Operational Research Vol.
    266, No. 2, pp. 761-774.
  5. Nadine Gatzert, A. Martin, M. Schmidt, B. Seith, N. Vogl (2020): Portfolio
    Optimization with Irreversible Long-Term Investments in Renewable Energy under
    Policy Risk: A Mixed-Integer Multistage Stochastic Model and a Moving-Horizon
    , in: European Journal of Operational Research forthcoming.
  6. Milevsky, M. A., & Salisbury, T. S. (2015). Optimal retirement income tontines.
    : Mathematics and Economics, 64, 91-105.
  7. Wüthrich, M. V. (2018). Machine learning in individual claims reserving.
    Scandinavian Actuarial Journal, 2018(6), 465-480.
  8. Wüthrich, M. V. (2018). Neural networks applied to chain–ladder reserving.
    European Actuarial Journal, 8(2), 407-436.