Valuation of Variable Annuities with Guaranteed Minimum Withdrawal Benefit under Stochastic Interest Rate

TL;DR

This paper introduces an efficient algorithm for pricing variable annuities with GMWB under stochastic interest rates, using advanced numerical methods and validated against known solutions, showing significant speed improvements.

Contribution

Develops a novel, fast algorithm for GMWB valuation under stochastic interest rates applicable to various models, enhancing computational efficiency and accuracy.

Findings

GMWB prices are higher with positive correlation between asset and interest rate.

The new algorithm outperforms finite difference and Monte Carlo methods in speed.

Results validated against closed-form solutions and Monte Carlo simulations.

Abstract

A variable annuity contract with Guaranteed Minimum Withdrawal Benefit (GMWB) promises to return the entire initial investment through cash withdrawals during the contract plus the remaining account balance at maturity, regardless of the portfolio performance. Under the optimal(dynamic) withdrawal strategy of a policyholder, GMWB pricing becomes an optimal stochastic control problem that can be solved by backward recursion of Bellman equation. In this paper we develop a very efficient new algorithm for pricing these contracts in the case of stochastic interest rate not considered previously in the literature. Presently our method is applied to the Vasicek interest rate model, but it is generally applicable to any model when transition density or moments of the underlying asset and interest rate are known in closed form or can be evaluated efficiently. Using bond price as a numeraire the required expectations in the backward recursion are reduced to two-dimensional integrals calculated through a high order Gauss-Hermite quadrature applied on a two-dimensional cubic spline interpolation. Numerical results from the new algorithm for a series of GMWB contracts for both static and optimal cases are presented. As a validation, results of the algorithm are compared with the closed form solutions for simple vanilla options, and with Monte Carlo and finite difference results for the static GMWB. The comparison demonstrates that the new algorithm is significantly faster than finite difference or Monte Carlo for all the two-dimensional problems tested so far. For dynamic GMWB pricing, we found that for positive correlation between the underlying asset and interest rate, the GMWB price under the stochastic interest rate is significantly higher compared to the case of deterministic interest rate, while for negative correlation the difference is less but still significant.