Journal Article

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

Valdez, Emiliano A., Gan, Guojun

2018

Overview

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.

• Publisher: MDPI AG
• Subject: Artificial intelligence / Cash flow forecasting / Computation / Computer simulation / Data mining / Datasets / Hedging / Insurance / Insurance companies / Insurance industry / Insurance premiums / Investments / metamodeling / Monte Carlo / Neural networks / portfolio valuation / regime-switching multivariate Black–Scholes / Researchers / Software / Synthetic data / Variable annuities / variable annuity