title: Improving option pricing with the product constrained hybrid neural network.
abstract: In the past decade, many studies across various financial markets have shown conventional option pricing models to be inaccurate. To improve their accuracy, various researchers have turned to artificial neural networks (ANNs). In this work a neural network is constrained in such a way that pricing must be rational at the option-pricing boundaries. The constraints serve to change the regression surface of the ANN so that option pricing accuracy is improved in the locale of the boundaries. These constraints lead to statistically and economically significant out-performance, relative to both the most accurate conventional and nonconventional option pricing models.
authors.affiliation: School of Business Systems, Monash University, Clayton, Victoria 3800, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Lajbcygier
mesh_terms: D018803:Models, Economic; D016571:Neural Networks, Computer