Palmer, S;
Gorse, D;
(2017)
Pseudo-analytical solutions for stochastic options pricing using monte carlo simulation and breeding PSO-trained neural networks.
In:
ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
(pp. pp. 365-370).
i6doc.com: Bruges, Belgium.
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Abstract
We introduce a novel methodology for pricing options which uses a particle swarm trained neural network to approximate the solution of a stochastic pricing model. The performance of the network is compared to the analytical solution for European call options and the errors shown statistically comparable to Monte Carlo pricing. The work provides a proof of concept that can be extended to more complex options for which no analytical solutions exist, the pricing method presented here delivering results several orders of magnitude faster than the Monte Carlo pricing method used by default in the financial industry.
| Type: | Proceedings paper |
|---|---|
| Title: | Pseudo-analytical solutions for stochastic options pricing using monte carlo simulation and breeding PSO-trained neural networks |
| Event: | ESANN 2017 |
| ISBN-13: | 9782875870391 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.i6doc.com/en/book/?gcoi=28001100477480 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10115864 |
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