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Enhancing valuation of variable annuities in Lévy models with stochastic interest rate

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  • Ludovic Goudenege
  • Andrea Molent
  • Xiao Wei
  • Antonino Zanette

Abstract

This paper extends the valuation and optimal surrender framework for variable annuities with guaranteed minimum benefits in a Lévy equity market environment by incorporating a stochastic interest rate described by the Hull-White model. This approach frames a more dynamic and realistic financial setting compared to previous literature. We exploit a robust valuation mechanism employing a hybrid numerical method that merges tree methods for interest rate modeling with finite difference techniques for the underlying asset price. This method is particularly effective for addressing the complexities of variable annuities, where periodic fees and mortality risks are significant factors. Our findings reveal the influence of stochastic interest rates on the strategic decision-making process concerning the surrender of these financial instruments. Through comprehensive numerical experiments, and by comparing our results with those obtained through the Longstaff-Schwartz Monte Carlo method, we illustrate how our refined model can guide insurers in designing contracts that equitably balance the interests of both parties. This is particularly relevant in discouraging premature surrenders while adapting to the realistic fluctuations of financial markets. Lastly, a comparative statics analysis with varying interest rate parameters underscores the impact of interest rates on the cost of the optimal surrender strategy, emphasizing the importance of accurately modeling stochastic interest rates.

Suggested Citation

  • Ludovic Goudenege & Andrea Molent & Xiao Wei & Antonino Zanette, 2025. "Enhancing valuation of variable annuities in Lévy models with stochastic interest rate," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2025(2), pages 213-235, February.
  • Handle: RePEc:taf:sactxx:v:2025:y:2025:i:2:p:213-235
    DOI: 10.1080/03461238.2024.2402816
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