Abstract
Option pricing is a process to obtain the theoretical fair value of an option based on the factors affecting its price. Currently, the nonparametric and computational methods of option valuation are able to construct a model of the pricing formula from historical data. However, these models are generally based on a global learning paradigm, which may not be able to efficiently and accurately capture the dynamics and time-varying characteristics of the option data. This paper proposes a novel brain-inspired cerebellar associative memory model for pricing American-style option on currency futures. The proposed model, called PSECMAC, constitute a local learning model that is inspired by the neurophysiological aspects of the human cerebellum. The PSECMAC-based option pricing model is subsequently applied in a mis-priced option arbitrage trading system. Simulation results show a return on investment as high as 23.1% for a relatively risk-free investment.
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Teddy, S.D., Lai, E.M.K., Quek, C. (2006). A Brain-Inspired Cerebellar Associative Memory Approach to Option Pricing and Arbitrage Trading. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_42
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DOI: https://doi.org/10.1007/11893295_42
Publisher Name: Springer, Berlin, Heidelberg
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