Variable annuities: A closer look at ratchet guarantees, hybrid contract designs, and taxation

ArXiv ID: 2507.07358 “View on arXiv”

Authors: Jennifer Alonso-Garcia, Len Patrick Dominic M. Garces, Jonathan Ziveyi

Abstract

This paper investigates optimal withdrawal strategies and behavior of policyholders in a variable annuity (VA) contract with a guaranteed minimum withdrawal benefit (GMWB) rider incorporating taxation and a ratchet mechanism for enhancing the benefit base during the life of the contract. Mathematically, this is accomplished by solving a backward dynamic programming problem associated with optimizing the discounted risk-neutral expectation of cash flows from the contract. Furthermore, reflecting traded VA contracts in the market, we consider hybrid products providing policyholders access to a cash fund which functions as an intermediate repository of earnings from the VA and earns interest at a contractually specified cash rate. We contribute to the literature by revealing several significant interactions among taxation, the cash fund, and the benefit base update mechanism. When tax rates are high, the tax-shielding effect of the cash fund, which is taxed differently from ordinary withdrawals from the VA, plays a significant role in enhancing the attractiveness of the overall contract. Furthermore, the ratchet benefit base update scheme (in contrast to the ubiquitous return-of-premium specification in the literature) tends to discourage early surrender as it provides enhanced downside market risk protection. In addition, the cash fund discourages active withdrawals, with policyholders preferring to transfer the guaranteed withdrawal amount to the cash fund to leverage the cash fund rate.

Keywords: Variable Annuities, GMWB, Dynamic Programming, Taxation, Cash Fund, Insurance/Annuities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper uses advanced mathematical techniques like backward dynamic programming and risk-neutral valuation to solve an optimal stopping problem, resulting in a high math score. However, the empirical evidence is primarily theoretical and relies on numerical experiments rather than real-world backtesting or heavy data implementation, leading to a low empirical rigor score.

  flowchart TD
    A["Research Goal:<br>Optimal Withdrawal Strategy<br>for Variable Annuities with GMWB<br>considering Taxation & Ratchet Mechanism"] --> B["Methodology:<br>Backward Dynamic Programming"]
    
    B --> C["Key Inputs & Data:"]
    C --> C1["Contract Design:<br>Ratchet vs Return-of-Premium"]
    C --> C2["Taxation Parameters:<br>Tax Rates & Cash Fund Rules"]
    C --> C3["Market Variables:<br>Interest Rates & Asset Returns"]
    
    C --> D["Computational Process:<br>Risk-Neutral Expectation<br>Maximization"]
    
    D --> E["Key Findings & Outcomes:"]
    E --> E1["Tax-Shielding Effect:<br>Cash fund enhances attractiveness<br>under high tax rates"]
    E --> E2["Ratchet Mechanism:<br>Reduces early surrender<br>via downside protection"]
    E --> E3["Behavioral Impact:<br>Cash fund discourages active<br>withdrawals (transfers preferred)"]