Black–Scholes versus artificial neural networks in pricing FTSE 100 options

Bennell, Julia A.; Sutcliffe, Charles

doi:10.1002/isaf.254

Cited by 61 publications

(10 citation statements)

References 35 publications

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“…[Insert Table 6 about here] Based on the above results, it is apparent that, for a given set of model inputs, the choice of the classification scheme is vital for the MCMNN model's superior performance. This evidence is in line with some other studies that focused on pricing of certain types of options (i.e., deep out-of-the-money options) and found that the classification of the data increases the pricing accuracy of the NN models (Gençay and Altay-Salih, 2003;Bennell and Sutcliffe, 2004). However, as shown in Table 6, one has to be cautious when using available domain knowledge to identify modules.…”

Section: [Insert Table 4 About Here]supporting

confidence: 87%

Multi-criteria classification for pricing European options

Gradojević¹

2015

Studies in Nonlinear Dynamics &Amp; Econometrics

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This paper builds a novel multi-criteria, non-parametric classification framework in order to improve the accuracy of pricing European options. The proposed approach is based on classifying financial options according to their implied volatility, time to maturity and moneyness. Using a recent data set for the daily S&P 500 index call options traded in 2012, the multi-criteria modular neural network model demonstrates its superior out-of-sample pricing performance relative to competing parametric and non-parametric models. By observing the model's pricing errors across various option types, the analysis provides additional insights into pricing biases and stresses the importance of selecting appropriate classification criteria.

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Section: [Insert Table 4 About Here]supporting

confidence: 87%

Multi-criteria classification for pricing European options

Gradojević¹

2015

Studies in Nonlinear Dynamics &Amp; Econometrics

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“…for out-of-the-money options, the BS performed better. In the following, Anders et al (1998), Garcia and Gençay (2000), Andreou et al (2002), Amilon (2003), Bennell and Sutcliffe (2004) and Andreou et al (2006) obtained similar encouraging results regarding European-style options. Boek et al (1995) and Lajbcygier et al (1997) proposed hybrid neural networks, which combine theoretical option models with NNs.…”

Section: Introductionsupporting

confidence: 61%

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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“…The input test data were then used to train the model using the weights estimated at the 200th time step. Tino et al (2001), Jasic and Wood (2004), Karathanasopoulos et al (2012b) and Bennell and Sutcliffe (2005) show results indicating that for all markets the improvement in the forecast by nonlinear models is significant and highly accurate. Moreover, Edelman (2008) presented a hybrid Calman filter-RBF model used in forecasting 1 day ahead the FTSE 100 and ISEQ.…”

Section: Literature Reviewmentioning

confidence: 90%

Modelling and Trading the English and German Stock Markets with Novelty Optimization Techniques

Karathanasopoulos

Skindilias

Lo³

2016

Journal of Forecasting

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The motivation for this paper was the introduction of novel short‐term models to trade the FTSE 100 and DAX 30 exchange‐traded funds (ETF) indices. There are major contributions in this paper which include the introduction of an input selection criterion when utilizing an expansive universe of inputs, a hybrid combination of partial swarm optimizer (PSO) with radial basis function (RBF) neural networks, the application of a PSO algorithm to a traditional autoregressive moving model (ARMA), the application of a PSO algorithm to a higher‐order neural network and, finally, the introduction of a multi‐objective algorithm to optimize statistical and trading performance when trading an index. All the machine learning‐based methodologies and the conventional models are adapted and optimized to model the index. A PSO algorithm is used to optimize the weights in a traditional RBF neural network, in a higher‐order neural network (HONN) and the AR and MA terms of an ARMA model. In terms of checking the statistical and empirical accuracy of the novel models, we benchmark them with a traditional HONN, with an ARMA, with a moving average convergence/divergence model (MACD) and with a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the FTSE 100 and DAX 30 ETF time series over the period January 2004 to December 2015 using the last 3 years for out‐of‐sample testing. Finally, the empirical and statistical results indicate that the PSO‐RBF model outperforms all other examined models in terms of trading accuracy and profitability, even with mixed inputs and with only autoregressive inputs. Copyright © 2016 John Wiley & Sons, Ltd.

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Black–Scholes versus artificial neural networks in pricing FTSE 100 options

Cited by 61 publications

References 35 publications

Multi-criteria classification for pricing European options

Multi-criteria classification for pricing European options

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

Modelling and Trading the English and German Stock Markets with Novelty Optimization Techniques

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