Hrsg.: Fraunhofer ITWM
2021, 167 S., num., col. illus. and tab., Softcover
Kaiserslautern, TU, Diss., 2021
This dissertation consists of three parts which have a major impact on the chance-risk classification of state-subsidized pension products in Germany. Firstly, we focus on the obtained yield curve shapes of the one- and two-factor Vasicek interest rate models. We show that the latter can explain significantly more effects observable at the market than the former. Further, we introduce a general change of measure framework for the Monte Carlo simulation of the Vasicek model under a subjective measure which takes the frequency of normal yield curves into account. Next, different time series models including machine learning algorithms forecasting the yield curve are examined. For the latter, we consider a fully connected feed-forward neural network and develop an extended approach for the hyperparameter optimization. In the last part, a procedure for determining the chance-risk class of a state-subsidized pension product portfolio under the constraint that the portfolio's chance-risk class does not exceed the customer's risk preference is developed. Furthermore, different approaches for determining the chance-risk class over the contract term of a pension product are shown.