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Option pricing and trading with artificial neural networks and advanced parametric models with implied parameters

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3 Author(s)
Panayiotis, A.C. ; Dept. of Public & Bus. Adm., Cyprus Univ., Lefkosia, Cyprus ; Spiros, M.H. ; Chris, C.

We combine parametric models and feedforward artificial neural networks to price and trade European S&P500 Index options. Artificial neural networks are optimized on a hybrid target function consisted by the standardized residual term between the actual market price and the option estimate of a certain parametric model. Parametric models include: (i) the Black and Scholes model that assumes a geometric Brownian motion process (GBM); (ii) the Corrado and Su that additionally allows for excess skewness and kurtosis via a Gram-Charlier series expansion; (iii) analytic models that extend the GBM by incorporating multiple sources of Poisson distributed jumps; and (vi) stochastic volatility and jump models. Daily average implied parameters of these models are estimated with options transaction data via an unconstraint process optimized by the non-linear least squares Levenberg-Marquardt algorithm. This structural average implied parameters are used to validate the out-of sample pricing and trading (with transaction costs) ability of all models developed.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004

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