Version 1
: Received: 28 June 2018 / Approved: 29 June 2018 / Online: 29 June 2018 (11:31:49 CEST)
How to cite:
Gan, G.; Valdez, E. Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets. Preprints2018, 2018060482. https://doi.org/10.20944/preprints201806.0482.v1
Gan, G.; Valdez, E. Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets. Preprints 2018, 2018060482. https://doi.org/10.20944/preprints201806.0482.v1
Gan, G.; Valdez, E. Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets. Preprints2018, 2018060482. https://doi.org/10.20944/preprints201806.0482.v1
APA Style
Gan, G., & Valdez, E. (2018). Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets. Preprints. https://doi.org/10.20944/preprints201806.0482.v1
Chicago/Turabian Style
Gan, G. and Emiliano Valdez. 2018 "Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets" Preprints. https://doi.org/10.20944/preprints201806.0482.v1
Abstract
Dynamic hedging has been adopted by many insurance companies to mitigate the financial risks associated with variable annuity guarantees. In order 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 3 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.
Keywords
Monte Carlo; regime-switching multivariate black-scholes; metamodeling; variable annuity; portfolio valuation
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
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.
