Pricing and Hedging Short Sterling Options Using Neural Networks

Chen, Fei; Sutcliffe, Charles

doi:10.1002/isaf.336

Cited by 17 publications

(9 citation statements)

References 60 publications

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“…2 ANN based option pricing and hedging in the literature Bennell and Sutcliffe [2004], Chen and Sutcliffe [2012], and Hahn [2013] 1 provide extensive literature surveys on the application of ANNs to option pricing and hedging problems. Here we complement these surveys with additional and more recent papers.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Neural networks for option pricing and hedging: a literature review

Ruf¹,

Wang²

2020

JCF

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“…We measured the performance of neural network approximations using R 2 , a measure has been used by other researchers to measure neural network accuracy, as in, e.g., Hattab et al (2013). There are, however, several other options for neural network performance metrics; a few such possibilities include correlation (Chen and Sutcliffe 2012), relative error (Hejazi and Jackson 2016), and mean square prediction error (Garcia and Gençay 2000). We expect that any of these metrics would have yielded substantially the same results.…”

Section: Pricingmentioning

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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“…Pioneers are Malliaris and Salchenberger (1993) and Hutchinson et al (1994), who compared the performance of the BS model and NNs in pricing American-style call options. For a detailed current overview about the range of prior studies, see Chen and Sutcliffe (2012). for out-of-the-money options, the BS performed better.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, subsequent studies mostly investigated daily equity index options data for option pricing approximations. For a detailed current overview about the range of prior studies, see Chen and Sutcliffe (2012).…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Pricing and Hedging of Options on Currency Futures with Artificial Neural Networks

Spreckelsen

Mettenheim

Breitner

2014

Journal of Forecasting

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High‐frequency trading and automated algorithm impose high requirements on computational methods. We provide a model‐free option pricing approach with neural networks, which can be applied to real‐time pricing and hedging of FX options. In contrast to well‐known theoretical models, an essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of real‐time input variables. To test its ability for the purpose of high‐frequency trading, we perform an empirical run‐time trading simulation with a tick dataset of EUR/USD options on currency futures of 4 weeks. In very short non‐overlapping 15‐minute out‐of‐sample intervals, theoretical option prices derived from the Black model compete against nonparametric option prices through two different neural network topologies. We show that the approximated pricing function of learning networks is suitable for generating fast run‐time option pricing evaluation as their performance is slightly better in comparison to theoretical prices. The derivation of the network function is also useful for performing hedging strategies. We conclude that the performance of closed‐form pricing models depends highly on the volatility estimator, whereas neural networks can avoid this estimation problem but require market liquidity for training. Nevertheless, we also have to take particular enhancements into account, which give us useful hints for further research and steps. Copyright © 2014 John Wiley & Sons, Ltd.

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Pricing and Hedging Short Sterling Options Using Neural Networks

Cited by 17 publications

References 60 publications

Neural networks for option pricing and hedging: a literature review

Neural networks for option pricing and hedging: a literature review

Using Neural Networks to Price and Hedge Variable Annuity Guarantees

Real‐Time Pricing and Hedging of Options on Currency Futures with Artificial Neural Networks

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