Special Aspects of Insurance Economics

Amount

Masterseminar 2/0 SWS (4 ECTS)

Dates

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

Further Information

If you have any questions, please contact

Content

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 Wirtschaftsmathematik, Wirtschaftswissenschaften or Finance. Previous knowledge in Personenversicherungsmathematik, and Derivatives can be 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
    results 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 three pages.
    Delivery of the presentation documents: at least one week before the presentation via email
    to an.chen(at)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. Carpenter, J. N. (2000). Does option compensation increase managerial risk appetite? The journal of finance, 55(5), 2311-2331.
  2. Dybvig, P. H. (1988). Distributional analysis of portfolio choice. Journal of Business, 369-393.
  3. Pastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The journal of Finance, 67(4), 1219-1264.
  4. Basak, S., & Shapiro, A. (2001). Value-at-risk-based risk management: optimal policies and asset prices. The review of financial studies, 14(2), 371-405.
  5. Kuo, K. (2019). DeepTriangle: A deep learning approach to loss reserving. Risks, 7(3), 97.
  6. Eckert, J., & Gatzert, N. (2018). Risk-and value-based management for non-life insurers under solvency constraints. European Journal of Operational Research, 266(2), 761-774.
  7. Wüthrich, M. V. (2018). Neural networks applied to chain–ladder reserving. European Actuarial Journal, 8(2), 407-436.
  8. Wüthrich, M. V. (2018). Machine learning in individual claims reserving. Scandinavian Actuarial Journal, 2018(6), 465-480.