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

Gaurav Khemka and Adam Butt
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Gaurav Khemka: Research School of Finance, Actuarial Studies and Applied Statistics, Building 26C, Kingsley Street, Australian National University, Canberra, ACT 2601, Australia
Adam Butt: Research School of Finance, Actuarial Studies and Applied Statistics, Building 26C, Kingsley Street, Australian National University, Canberra, ACT 2601, Australia

Risks, 2017, vol. 5, issue 4, 1-17

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.

Keywords: data-driven; quadrature; Quasi-Monte Carlo; retirement (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:5:y:2017:i:4:p:57-:d:117091

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