Artikel
Non-parametric integral estimation using data clustering in stochastic dynamic programming: An introduction using lifetime financial modelling
This paper considers an alternative way of structuring stochastic variables in a dynamic programming framework where the model structure dictates that numerical methods of solution are necessary. Rather than estimating integrals within a Bellman equation using quadrature nodes, we use nodes directly from the underlying data. An example of the application of this approach is presented using individual lifetime financial modelling. The results show that data-driven methods lead to the least losses in result accuracy compared to quadrature and Quasi-Monte Carlo approaches, using historical data as a base. These results hold for both a single stochastic variable and multiple stochastic variables. The results are significant for improving the computational accuracy of lifetime financial models and other models that employ stochastic dynamic programming.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 5 ; Year: 2017 ; Issue: 4 ; Pages: 1-17 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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data-driven
quadrature
Quasi-Monte Carlo
retirement
- Event
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Geistige Schöpfung
- (who)
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Khemka, Gaurav
Butt, Adam
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2017
- DOI
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doi:10.3390/risks5040057
- Handle
- Last update
- 10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Khemka, Gaurav
- Butt, Adam
- MDPI
Time of origin
- 2017
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