Deep Hedging: Learning to Simulate Equity Option Markets

Wiese, Magnus; Bai, Linquan; Wood, B. Dan; Buehler, Hans

doi:10.2139/ssrn.3470756

Cited by 42 publications

(20 citation statements)

References 13 publications

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“…Such simulation engines could then be used for option pricing and hedging, a direction still to be explored systematically. Just after finishing this survey, Wiese et al [2019a] proposed a generative ANN for option prices (instead of stock prices).…”

Section: Further Workmentioning

confidence: 99%

Neural networks for option pricing and hedging: a literature review

Ruf¹,

Wang²

2020

JCF

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Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

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Section: Further Workmentioning

confidence: 99%

Neural networks for option pricing and hedging: a literature review

Ruf¹,

Wang²

2020

JCF

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“…Here the goal is to precisely mimic the behavior and features of historical market trajectories. This line of research has been recently pursued in e.g., Kondratyev and Schwarz (2019); Wiese et al (2019); Acciaio and Xu (2020); Bühler et al 2020; Henry-Labordère (2019).…”

Section: Generative Adversarial Approaches In Financementioning

confidence: 99%

A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models

Cuchiero

Khosrawi

Teichmann

2020

Risks

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We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, possibly in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows the calculation of model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analysis on many samples of implied volatility smiles, showing the accuracy and stability of the method.

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“…However, recently they have been applied to time-series data. Currently GANs are applied to various domains for generating realistic time-series data including health care [3,7,10,18,23] ,finance [21,22] , and energy industry [1,4,25]. In [24], authors combine GAN and auto-regressive models to improve sequential data generation.…”

Section: Related Workmentioning

confidence: 99%

If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN

Koochali¹,

Dengel²,

Ahmed³

2020

Preprint

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The contribution of this paper is two-fold. First, we present ProbCast -a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a framework that lets us transform a deterministic model into a probabilistic one with improved performance. The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently. We conduct experiments over two publicly available datasets namely electricity consumption dataset and exchange-rate dataset. The results of the experiments demonstrate the remarkable performance of our model as well as the successful application of our proposed framework.

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Deep Hedging: Learning to Simulate Equity Option Markets

Cited by 42 publications

References 13 publications

Neural networks for option pricing and hedging: a literature review

Neural networks for option pricing and hedging: a literature review

A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models

If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN

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