Deep hedging of long-term financial derivatives

Carbonneau, Alexandre

doi:10.1016/j.insmatheco.2021.03.017

Cited by 15 publications

(16 citation statements)

References 40 publications

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“…Further, our paper contributes to the recent literature on deep learning approaches in hedging, starting from the seminal work Buehler et al (2019) and followed by Gümbel and Schmidt (2020), Cuchiero et al (2020), Cao et al (2021), Carbonneau (2021), Carbonneau and Godin (2021), Chen and Wan (2021), Horváth et al (2021), Neufeld and Sester (2021a), amongst many others (see also Ruf and Wang (2020) for a review).…”

Section: Introductionmentioning

confidence: 93%

Robust deep hedging

Lütkebohmert¹,

Schmidt²,

Sester³

2021

Preprint

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We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework.The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches, in particular in highly volatile periods.

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Section: Introductionmentioning

confidence: 93%

Robust deep hedging

Lütkebohmert¹,

Schmidt²,

Sester³

2021

Preprint

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“…Small enterprises do not have the ability and resources to use them, and enterprises with insufficient strength may suffer losses due to insufficient investment. In addition, if the return of using derivatives for hedging is even less than that of ordinary risk management, enterprises will abandon it because of higher costs [23]. Therefore, to promote the prosperity and development of China derivatives market, these important problems must be solved.…”

Section: Inspiration and Prospectmentioning

confidence: 99%

Financial Derivatives and Their Application in Enterprises

Huang¹,

Yao²

2021

Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)

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With the opening of the financial market, the financial derivatives market has also encountered greater development opportunities. However, opportunities and challenges always coexist, so enterprises must use financial derivatives reasonably and correctly. This paper adopts the method of combining theoretical analysis with case analysis to introduce the types, risks, applicable mechanisms and typical application cases of financial derivatives in detail. In addition, through detailed descriptions of the theoretical underpinning, the reader could learn about the trading processes and profitability mechanisms of several major derivatives, as well as conducting immersive simulations through case studies. Through in-depth analysis and research, it is concluded that enterprises should actively invest in technology and resource support, use derivatives to control risks and realize enterprise value. Hedging, rather than random speculation, should be the first rule of thumb for companies using derivatives. It is hoped that the research of this paper can provide a good way for existing enterprises to use derivatives to avoid risks and help the financial derivatives market achieve steady development.

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“…Conversely, Carbonneau and Godin (2021b) and Carbonneau and Godin (2021a) use the deep reinforcement learning approach of Buehler et al (2019) coined as deep hedging. Other papers have relied on the deep hedging methodology for the hedging of financial derivatives: Cao et al (2020), Carbonneau (2021), Horvath et al (2021) and Lütkebohmert et al (2021). Deep reinforcement learning is a very favorable technique for multistage optimization and decision-making in financial contexts: it allows tackling high-dimensional settings with multiple state variables, underlying asset dynamics and trading instruments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This avenue is thus successful to alleviate the price inflation phenomenon when using ERP procedures for the pricing of long-term options. It is worth highlighting that in the presence of jump risk, the use of options as hedging instruments is much more effective for risk mitigation as compared to hedging strategies involving exclusively the underlying stock (see for instanceColeman et al (2007) andCarbonneau (2021)). Nevertheless, C 0 values presented in Table6indicate that when setting up trading strategies with options is impractical due to high expected trading costs, the use of stock hedges coupled with semi-L p risk measures can effectively reduce option prices.…”

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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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Deep hedging of long-term financial derivatives

Cited by 15 publications

References 40 publications

Robust deep hedging

Robust deep hedging

Financial Derivatives and Their Application in Enterprises

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

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