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Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets

Department of Mathematics, University of Connecticut, 341 Mansfield Road, Storrs, CT 06269-1009, USA
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Received: 11 July 2018 / Revised: 24 August 2018 / Accepted: 30 August 2018 / Published: 1 September 2018
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Abstract

Dynamic hedging has been adopted by many insurance companies to mitigate the financial risks associated with variable annuity guarantees. To simulate the performance of dynamic hedging for variable annuity products, insurance companies rely on nested stochastic projections, which is highly computationally intensive and often prohibitive for large variable annuity portfolios. Metamodeling techniques have recently been proposed to address the computational issues. However, it is difficult for researchers to obtain real datasets from insurance companies to test metamodeling techniques and publish the results in academic journals. In this paper, we create synthetic datasets that can be used for the purpose of addressing the computational issues associated with the nested stochastic valuation of large variable annuity portfolios. The runtime used to create these synthetic datasets would be about three years if a single CPU were used. These datasets are readily available to researchers and practitioners so that they can focus on testing metamodeling techniques. View Full-Text
Keywords: Monte Carlo; regime-switching multivariate Black–Scholes; metamodeling; variable annuity; portfolio valuation Monte Carlo; regime-switching multivariate Black–Scholes; metamodeling; variable annuity; portfolio valuation
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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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MDPI and ACS Style

Gan, G.; Valdez, E.A. Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets. Data 2018, 3, 31.

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