A brain-inspired cerebellar associative memory approach to option pricing and arbitrage trading

  • Authors:

  • S. D. Teddy;E. M.-K. Lai;C. Quek

  • Affiliations:

  • School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore;School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore;School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore

  • Venue:
  • ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
  • Year:
  • 2006

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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.