Title

A Neural Network Model for Estimating Option Prices

Document Type

Article

Language

eng

Publication Date

9-1993

Publisher

Springer

Source Publication

Applied Intelligence

Source ISSN

0924-669X

Abstract

A neural network model that processes financial input data is developed to estimate the market price of options at closing. The network's ability to estimate closing prices is compared to the Black-Scholes model, the most widely used model for the pricing of options. Comparisons reveal that the mean squared error for the neural network is less than that of the Black-Scholes model in about half of the cases examined. The differences and similarities in the two modeling approaches are discussed. The neural network, which uses the same financial data as the Black-Scholes model, requires no distribution assumptions and learns the relationships between the financial input data and the option price from the historical data. The option-valuation equilibrium model of Black-Scholes determines option prices under the assumptions that prices follow a continuous time path and that the instantaneous volatility is nonstochastic.

Comments

Applied Intelligence, Vol. 3, No. 3 (September 1993): 193-206. DOI.

Linda Salchenberger was associated with Loyola University Chicago at the time of publication.

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