The option pricing model based on time values: an application of the universal approximation theory on unbounded domains
This work addresses option pricing for financial modeling, but it is incremental as it builds on an existing model with specific improvements.
The authors tackled the classical option pricing problem by proposing a time value-related decision function, which significantly improved the original Hutchinson-Lo-Poggio model with faster convergence and better generalization performance in numerical experiments.
We propose a time value related decision function to treat a classical option pricing problem raised by Hutchinson-Lo-Poggio. In numerical experiments, the new decision function significantly improves the original model of Hutchinson-Lo-Poggio with faster convergence and better generalization performance. By proving a novel universal approximation theorem, we show that our decision function rather than Hutchinson-Lo-Poggio's can be approximated on the entire domain of definition by neural networks. Thus the experimental results are partially explained by the representation properties of networks.