Yield Curves and Chance-Risk Classification: Modeling, Forecasting, and Pension Product Portfolios
Seiten
This dissertation deals with three topics: Yield curve modeling, forecasting, and the application of the chance-risk classification to a pension product portfolio. As a component of the capital market model, the yield curve influences the chance-risk classification which was introduced to improve the comparability of pension products and strengthen consumer protection. Thus, all three topics have a major impact on this essential safeguard.
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.
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.
| Erscheinungsdatum | 24.03.2022 |
|---|---|
| Zusatzinfo | num., col. illus. and tab. |
| Verlagsort | Stuttgart |
| Sprache | englisch |
| Maße | 148 x 210 mm |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| Schlagworte | Aktuare • B • Chance-Risk Classification of pension products • Finanz- und Versicherungsmathematiker • Fraunhofer ITWM • Neural networks • Time Series Analysis • Vasicek models Change of measure |
| ISBN-10 | 3-8396-1767-7 / 3839617677 |
| ISBN-13 | 978-3-8396-1767-0 / 9783839617670 |
| Zustand | Neuware |
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