GenSo-OPATS: A brain-inspired dynamically evolving option pricing model and arbitrage trading system

Abstract

Obtaining the theoretical fair value of an option based on the factors affecting its price is a process called option pricing and commonly known approaches are the Black-Scholes formula and the binomial pricing model. However, these parametric models are generally dependent on the assumptions of continuous-time finance theory and presumed complex and rigid statistical formulations. Nonparametric and computational methods of option pricing, on the other hand, are able to accurately model the pricing formula from historical data but suffer from poor interpretability due to their opaque architectures. Generally, there is no guarantee that the prices derived from these model-free approaches conform to rational pricing. This paper proposes a novel brain-inspired nonparametric model for pricing American-style option on currency futures based on a dynamically evolving semantic memory model named GenSoFNN-TVR(S). Logical reasoning rules governing the pricing decisions can be extracted from the proposed model. Subsequently, the GenSoFNN-TVR(S) based option pricing model is implemented in a mis-priced option arbitrage trading system named GenSo-OPATS, and simulation results demonstrated an encouraging rate of return on investment.