A Brain-Inspired Cerebellar Associative Memory Approach to Option Pricing and Arbitrage Trading
نویسندگان
چکیده
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. Subsequently, the PSECMAC-based option pricing model is used in a mis-priced option arbitrage trading system and simulation results demonstrated an encouraging rate of return on investment.
منابع مشابه
American Option Pricing of Future Contracts in an Effort to Investigate Trading Strategies; Evidence from North Sea Oil Exchange
In this paper, Black Scholes’s pricing model was developed to study American option on future contracts of Brent oil. The practical tests of the model show that market priced option contracts as future contracts less than what model did, which mostly represent option contracts with price rather than without price. Moreover, it suggests call option rather than put option. Using t hypothesis test...
متن کاملVector majorization and a robust option
We show that vector majorization and its related preference sets can be used to establish useful option pricing bounds for a robust option replacement investment strategy. This robust trading strategy can help to overcome some of the difficulties in implementing arbitrage option trading strategies when there exists model inaccuracy.
متن کاملStatistical arbitrage trading with wavelets and artificial neural networks
The paper outlines the use of an altemative option pricing scheme to perform statistical arbitrage in derivative markets. The method links a binomial tree to an innovative stochastic volatility model that is based on wavelets and artificial neural networks. Wavelets provide a convenient signalhoise decomposition of volatility in a non-linear feature space. Neural networks are used to infer futu...
متن کاملLearning Martingale Measures From High Frequency Financial Data to Help Option Pricing
We provide a framework for learning risk-neutral measures (Martingale measures) for pricing options from high frequency financial data. In a simple geometric Brownian motion model, a price volatility, a fixed interest rate and a no-arbitrage condition suffice to determine a unique risk-neutral measure. On the other hand, in our framework, we relax some of these assumptions to obtain a class of ...
متن کاملLearning Martingale Measures to Price Options
We provide a framework for learning risk-neutral measures (Martingale measures) for pricing options. In a simple geometric Brownian motion model, the price volatility, fixed interest rate and a no-arbitrage condition suffice to determine a unique risk-neutral measure. On the other hand, in our framework, we relax some of these assumptions to obtain a class of allowable risk-neutral measures. We...
متن کامل