Abstract
It is well known that conventional option pricing models have systematic, statistically and economically significant errors or residuals. In this work an artificial neural network (ANN), which estimates the residuals from the most accurate conventional option pricing model, so as to improve option pricing accuracy, is constrained in such a way so that pricing must be rational at the option-pricing boundaries. These constraints lead to statistically and economically significant out-performance relative to both the most accurate conventional and non-constrained ANN option pricing models.
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Lajbcygier, P. (2003). Option Pricing with the Product Constrained Hybrid Neural Network. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_73
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DOI: https://doi.org/10.1007/3-540-44989-2_73
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