Path Generation Methods for Valuation of Large Variable Annuities Portfolio using Quasi-Monte Carlo Simulation

Abstract

Variable annuities are long-term insurance products that offer a large variety of investment-linked benefits, which have gained much popularity in the last decade. Accurate valuation of large variable annuity portfolios is an essential task for insurers. However, these products often have complicated payoffs that depend on both of the policyholder’s mortality risk and the financial market risk. Consequently, their values are usually estimated by computationally intensive Monte Carlo simulation. Simulating large numbers of sample paths from complex dynamic asset models is often a computational bottleneck. In this study, we propose and analyze three Quasi-Monte Carlo path generation methods, Cholesky decomposition, Brownian Bridge, and Principal Component Analysis, for the valuation of large VA portfolios. Our numerical results indicate that all three PGMs produce more accurate estimates than the standard Monte Carlo simulation at both the contract and portfolio levels.