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Risks 2017, 5(4), 57; doi:10.3390/risks5040057

Non-Parametric Integral Estimation Using Data Clustering in Stochastic dynamic Programming: An Introduction Using Lifetime Financial Modelling

†,* and
Research School of Finance, Actuarial Studies and Applied Statistics, Building 26C, Kingsley Street, Australian National University, Canberra, ACT 2601, Australia
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 6 October 2017 / Revised: 26 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
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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. View Full-Text
Keywords: data-driven; quadrature; Quasi-Monte Carlo; retirement data-driven; quadrature; Quasi-Monte Carlo; retirement

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Khemka, G.; Butt, A. Non-Parametric Integral Estimation Using Data Clustering in Stochastic dynamic Programming: An Introduction Using Lifetime Financial Modelling. Risks 2017, 5, 57.

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