Abstract
As soon as one is ready to accept the homogeneity of degree one of the option price with respect to the underlying stock price and the strike price, the resulting option pricing formula keeps the main functional shape of the usual Black-Scholes formula. The generalized option pricing formula nests most of the usual parametric option pricing formulas. We show that pricing accuracy gains can be made by exploiting the implications of this homogeneity property in a nonparametric framework. The results indicate that the homogeneity hint always reduces the out-of-sample mean squared prediction error compared with a feedforward neural network with no hint.
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© 1998 Springer Science+Business Media Dordrecht
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Garcia, R., Gençay, R. (1998). Option Pricing with Neural Networks and a Homogeneity Hint. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_15
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_15
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