Option Pricing Using Neural Networks

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dc.contributor.advisorBadescu, Alexandru M.
dc.contributor.authorQue, Danfeng
dc.contributor.committeememberWare, Antony Frank
dc.contributor.committeememberSwishchuk, Anatoliy V.
dc.date2019-11
dc.date.accessioned2019-09-06T16:51:28Z
dc.date.available2019-09-06T16:51:28Z
dc.date.issued2019-08-30
dc.description.abstractDue to the properties of large transaction volumes, high innovation and positive promotion to the financial market development, options play an essential role. However, the flexible design of options leads to complicated pricing, which makes accurate option pricing a challenging task for a long time. In this paper, two types of neural networks - feed-forward networks such as the radial basis function (RBF), multilayer perceptron (MLP), Modular network and a recurrent deep learning network Long short-term memory (LSTM) - and three stochastic process pricing models (Black-Scholes-Merton model, Heston stochastic volatility model and Merton Jump-diffusion model) are proposed so as to predict European call option prices. Firstly, it generates simulated option prices data from three stochastic process models to test the effectiveness and approximation ability of the neural networks. Secondly, effective factors such as moneyness, time to maturity and greeks via the Black-Scholes-Merton formula are used as input variables for neural networks. Historical data of S&P 500 European option prices empirically analyzes validity and stability of the neural networks. The performance measures R2, statistical error of the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of two types of pricing models. It shows that the MLP network with two hidden layers performs best. In addition, neural networks do outperform the pricing ability of stochastic process pricing models.en_US
dc.identifier.citationQue, D. (2019). Option Pricing Using Neural Networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/36945
dc.identifier.urihttp://hdl.handle.net/1880/110867
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectStochastic processen_US
dc.subjectfeed forward neural networken_US
dc.subjectrecurrent neural networken_US
dc.subjectoption pricingen_US
dc.subject.classificationEducation--Financeen_US
dc.subject.classificationMathematicsen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.titleOption Pricing Using Neural Networksen_US
dc.typemaster thesisen_US
thesis.degree.disciplineMathematics & Statisticsen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopyfalseen_US
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