Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions

Gan, Guojun

doi:10.3390/risks6030071

Cited by 17 publications

(3 citation statements)

References 25 publications

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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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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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“…To speed up the valuation of VA portfolios based on Monte Carlo simulation, metamodelling techniques have been proposed in the past few years. See Gan & Lin (2015), Gan & Valdez (2017a), Hejazi et al (2017), Gan (2018), Xu et al (2018), Dang et al (2019), Liu & Tan (2020), Gweon et al (2020), Yang (2020), andFeng et al (2020). Metamodelling techniques involve building a predictive model based on a small number of representative VA policies in order to reduce the number of policies that are valued by Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

Tree-based models for variable annuity valuation: parameter tuning and empirical analysis

2021

Self Cite

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Variable annuities have become popular retirement and investment vehicles due to their attractive guarantee features. Nonetheless, managing the financial risks associated with the guarantees poses great challenges for insurers. One challenge is risk quantification, which involves frequent valuation of the guarantees. Insurers rely on the use of Monte Carlo simulation for valuation as the guarantees are too complicated to be valued by closed-form formulas. However, Monte Carlo simulation is computationally intensive. In this paper, we empirically explore the use of tree-based models for constructing metamodels for the valuation of the guarantees. In particular, we consider traditional regression trees, tree ensembles, and trees based on unbiased recursive partitioning. We compare the performance of tree-based models to that of existing models such as ordinary kriging and generalised beta of the second kind (GB2) regression. Our results show that tree-based models are efficient in producing accurate predictions and the gradient boosting method is considered the most superior in terms of prediction accuracy.

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“…In a similar vein, Gan (2018) used a metamodel approach consisting of a linear model with interactions, and using an overlapped group lasso method (Lim and Hastie 2015) to help with the selection of variable interactions for the model. For a review of the various types of metamodels that have been used in this capacity and a discussion of their advantages, see Gan (2018).…”

Section: Introductionmentioning

confidence: 99%

Using Neural Networks to Price and Hedge Variable Annuity Guarantees

Doyle¹,

Groendyke

2018

Risks

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This paper explores the use of neural networks to reduce the computational cost of pricing and hedging variable annuity guarantees. Pricing these guarantees can take a considerable amount of time because of the large number of Monte Carlo simulations that are required for the fair value of these liabilities to converge. This computational requirement worsens when Greeks must be calculated to hedge the liabilities of these guarantees. A feedforward neural network is a universal function approximator that is proposed as a useful machine learning technique to interpolate between previously calculated values and avoid running a full simulation to obtain a value for the liabilities. We propose methodologies utilizing neural networks for both the tasks of pricing as well as hedging four different varieties of variable annuity guarantees. We demonstrated a significant efficiency gain using neural networks in this manner. We also experimented with different error functions in the training of the neural networks and examined the resulting changes in network performance.

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Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions

Cited by 17 publications

References 25 publications

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

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

Tree-based models for variable annuity valuation: parameter tuning and empirical analysis

Using Neural Networks to Price and Hedge Variable Annuity Guarantees

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