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Non-Parametric Integral Estimation Using Data Clustering in Stochastic dynamic Programming: An Introduction Using Lifetime Financial Modelling
- Source :
- Risks, Vol 5, Iss 4, p 57 (2017)
- Publication Year :
- 2017
- Publisher :
- MDPI AG, 2017.
-
Abstract
- 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.
- Subjects :
- data-driven
quadrature
Quasi-Monte Carlo
retirement
Insurance
HG8011-9999
Subjects
Details
- Language :
- English
- ISSN :
- 22279091
- Volume :
- 5
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Risks
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.4647b02145e4486895e807b84ae96420
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/risks5040057