Efficient Simulation Designs for Valuation of Large Variable Annuity Portfolios

Feng, Mingbin; Tan, Zhen; Zheng, Jiayi

doi:10.1080/10920277.2019.1685394

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Moreover, Liu and Tan (2020) show that their "green mesh" approach is itself strongly preferable to the clustering-kriging method proposed initially by Gan (2013). And even the improvements to the latter by Feng et al (2020) produce estimation results that are substantially inferior to the RDP method (see table 3 in their paper). Gan and Valdez (2020) are able to improve on that with a rank order kriging (also known as quantile kriging) approach.…”

Section: Resultsmentioning

confidence: 95%

“…Recent studies have attempted to remedy this issue by developing valuation methods specifically for VA portfolios, typically using machine learning methods (Hejazi & Jackson, 2016;Xu et al, 2018) or metamodeling techniques that rely on the selection of a small number of representative contracts that are valued in detail (e.g., using simulation), and then extending the values to the portfolio using spatial interpolation or regression methods based on the policies' characteristics (see e.g., Feng et al, 2020;Gan, 2013;Gan & Lin, 2015;Gan & Valdez, 2018;Hejazi et al, 2017;Liu & Tan, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Efficient valuation of variable annuity portfolios with dynamic programming

Moenig

2021

J of Risk & Insurance

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The valuation of variable annuity portfolios presents major challenges for US life insurers. Recent studies propose machine learning and metamodeling techniques based on selecting a few representative guarantees. However, these methods face a critical trade-off between speed and accuracy. In contrast, I propose a recursive dynamic programming approach and demonstrate its ability to value a large and highly heterogeneous variable annuity portfolio with a high degree of accuracy and within a few seconds-even under stochastic interest rates and volatility-since the heavy computational burden can be fully front-loaded (in a one-time effort at the guarantee's pricing stage). This makes the dynamic programming approach ideally suited for all variable annuity applications, including the computation of reserves and capital requirements and to determine the insurer's hedging position. Moreover, dynamic programming can naturally incorporate optimal policyholder behavior into the insurer's valuation.

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Section: Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Efficient valuation of variable annuity portfolios with dynamic programming

Moenig

2021

J of Risk & Insurance

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“…To further boost the efficiency and the effectiveness of selecting and pricing the representative contracts, as well as valuating the whole portfolio, various methods at each of these three steps have been proposed. See, for instance, Gan and Lin (2015), Hejazi and Jackson (2016), Gan and Valdez (2018), Gan (2018), Gan and Valdez (2020), Gweon et al (2020), Liu and Tan (2020), Lin and Yang (2020), Feng et al (2020), and Quan et al (2021). Similar idea has also been applied to the calculation of Greeks and risk measures of a portfolio of variable annuities; see Gan and Lin (2017), Gan and Valdez (2017), Xu et al (2018), and Dang et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning

Chong¹,

Cui²,

Li³

2021

Preprint

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This paper applies a deep reinforcement learning approach to revisit the hedging problem of variable annuities. Instead of assuming actuarial and financial dualmarket model a priori, the reinforcement learning agent learns how to hedge by collecting anchor-hedging reward signals through interactions with the market. By the recently advanced proximal policy optimization, the pseudo-model-free reinforcement learning agent performs equally well as the correct Delta, while outperforms the misspecified Deltas. The reinforcement learning agent is also integrated with online learning to demonstrate its full adaptive capability to the market.

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“…For instance, Gan & Lin (2015) extended the ordinary kriging method to the universal kriging method; Hejazi & Jackson (2016) used a neural network as the predictive model to valuate the whole portfolio; Gan & Valdez (2018) implemented the generalised beta of the second kind method instead of the kriging method to capture the non-Gaussian behaviour of the market price of variable annuities. See also, Gan (2018), Gan & Valdez (2020), Gweon et al (2020), Liu & Tan (2020), Lin & Yang (2020), Feng et al (2020), and Quan et al (2021) for recent developments in this three-step technique. Similar idea has also been applied to the calculation of Greeks and risk measures of a portfolio of variable annuities; see Gan & Lin (2017), Gan & Valdez (2017), and Xu et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Pseudo-model-free hedging for variable annuities via deep reinforcement learning

Chong

Cui

2023

Ann. actuar. sci.

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This paper proposes a two-phase deep reinforcement learning approach, for hedging variable annuity contracts with both GMMB and GMDB riders, which can address model miscalibration in Black-Scholes financial and constant force of mortality actuarial market environments. In the training phase, an infant reinforcement learning agent interacts with a pre-designed training environment, collects sequential anchor-hedging reward signals, and gradually learns how to hedge the contracts. As expected, after a sufficient number of training steps, the trained reinforcement learning agent hedges, in the training environment, equally well as the correct Delta while outperforms misspecified Deltas. In the online learning phase, the trained reinforcement learning agent interacts with the market environment in real time, collects single terminal reward signals, and self-revises its hedging strategy. The hedging performance of the further trained reinforcement learning agent is demonstrated via an illustrative example on a rolling basis to reveal the self-revision capability on the hedging strategy by online learning.

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Efficient Simulation Designs for Valuation of Large Variable Annuity Portfolios

Cited by 10 publications

References 18 publications

Efficient valuation of variable annuity portfolios with dynamic programming

Efficient valuation of variable annuity portfolios with dynamic programming

Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning

Pseudo-model-free hedging for variable annuities via deep reinforcement learning

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