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

Chong, Wing Fung; Cui, Haoen; Li, Yuxuan

doi:10.48550/arxiv.2107.03340

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…For this reason, it was used in multiple other works on derivatives pricing and hedging. Various techniques were considered, such as Q-learning by Halperin (2020) and Cao et al (2021), proximal policy optimization by Chong et al (2021), least squares policy iteration and fitted Q-iteration for American option pricing by Li et al (2009), or batch policy gradient by Buehler et al (2019). Moreover, various other financial problems were tackled through reinforcement learning procedures in the literature, for instance, portfolio management by Moody and Wu (1997), Jiang et al (2017), Pendharkar and Cusatis (2018), García-Galicia et al (2019), Wang and Zhou (2020), Ye et al (2020) and Betancourt and Chen (2021); optimal liquidation by Bao and Liu (2019); or trading optimization by Hendricks and Wilcox (2014), Lu (2017) and Ning et al (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures

Carbonneau

Godin

2023

Risks

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The objective is to study the use of non-translation invariant risk measures within the equal risk pricing (ERP) methodology for the valuation of financial derivatives. The ability to move beyond the class of convex risk measures considered in several prior studies provides more flexibility within the pricing scheme. In particular, suitable choices for the risk measure embedded in the ERP framework, such as the semi-mean-square-error (SMSE), are shown herein to alleviate the price inflation phenomenon observed under the tail value at risk-based ERP as documented in previous work. The numerical implementation of non-translation invariant ERP is performed through deep reinforcement learning, where a slight modification is applied to the conventional deep hedging training algorithm so as to enable obtaining a price through a single training run for the two neural networks associated with the respective long and short hedging strategies. The accuracy of the neural network training procedure is shown in simulation experiments not to be materially impacted by such modification of the training algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures

Carbonneau

Godin

2023

Risks

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“…For this reason, it was used in multiple other works on derivatives pricing and hedging. Various techniques were considered such as Q-learning in Halperin (2020) and Cao et al (2021), proximal policy optimization in Chong et al (2021), least squares policy iteration and fitted Q-iteration for American option pricing in Li et al (2009), or batch policy gradient in Buehler et al (2019). Moreover, various other financial problems were tackled through reinforcement learning procedures in the literature, for instance portfolio management as in Moody and Wu (1997), Jiang et al (2017), Pendharkar and Cusatis (2018), García-Galicia et al (2019), Wang and Zhou (2020), Ye et al (2020) and Betancourt and Chen (2021), optimal liquidation, see Bao and Liu (2019), or trading optimization as in Hendricks and Wilcox (2014), Lu (2017) and Ning et al (2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep equal risk pricing of financial derivatives with non-translation invariant risk measures

Carbonneau¹,

Godin²

2021

Preprint

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The use of non-translation invariant risk measures within the equal risk pricing (ERP) methodology for the valuation of financial derivatives is investigated. The ability to move beyond the class of convex risk measures considered in several prior studies provides more flexibility within the pricing scheme. In particular, suitable choices for the risk measure embedded in the ERP framework such as the semi-mean-square-error (SMSE) are shown herein to alleviate the price inflation phenomenon observed under Tail Value-at-Risk based ERP as documented for instance in Carbonneau and Godin (2021b). The numerical implementation of non-translation invariant ERP is performed through deep reinforcement learning, where a slight modification is applied to the conventional deep hedging training algorithm (see Buehler et al., 2019) so as to enable obtaining a price through a single training run for the two neural networks associated with the respective long and short hedging strategies. The accuracy of the neural network training procedure is shown in simulation experiments not to be materially impacted by such modification of the training algorithm.

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“…Carbonneau (2021) uses the methodology in Buehler et al (2019) and studies approaches to risk management of long-term financial derivatives motivated by guarantees and options embedded in life-insurance products. Another approach to deep hedging based on reinforcement learning for managing risks stemming from long-term life-insurance products is presented in Chong et al (2021). Dynamic pricing has been studied extensively in the operations research literature.…”

Section: Introductionmentioning

confidence: 99%

Premium control with reinforcement learning

Palmborg

Lindskog

2023

ASTIN Bull.

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We consider a premium control problem in discrete time, formulated in terms of a Markov decision process. In a simplified setting, the optimal premium rule can be derived with dynamic programming methods. However, these classical methods are not feasible in a more realistic setting due to the dimension of the state space and lack of explicit expressions for transition probabilities. We explore reinforcement learning techniques, using function approximation, to solve the premium control problem for realistic stochastic models. We illustrate the appropriateness of the approximate optimal premium rule compared with the true optimal premium rule in a simplified setting and further demonstrate that the approximate optimal premium rule outperforms benchmark rules in more realistic settings where classical approaches fail.

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

Cited by 3 publications

References 41 publications

Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures

Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures

Deep equal risk pricing of financial derivatives with non-translation invariant risk measures

Premium control with reinforcement learning

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