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Option Pricing With Modular Neural Networks

Nikola Gradojevic

IÉSEG School of Management; University of Bologna - Rimini Center for Economic Analysis (RCEA)

Ramazan Gencay

Simon Fraser University


This paper applies a non-parametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogenous of degree one with respect to the underlying index price and the strike price. We find that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint), relative to the Black-Scholes model.

Number of Pages in PDF File: 25

Keywords: Option Pricing, Modular Neural Networks, Non-parametric Methods.

JEL Classification: C45, G12

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Date posted: January 27, 2007  

Suggested Citation

Gradojevic, Nikola and Gencay, Ramazan, Option Pricing With Modular Neural Networks (2007). Available at SSRN: http://ssrn.com/abstract=959544 or http://dx.doi.org/10.2139/ssrn.959544

Contact Information

Nikola Gradojevic (Contact Author)
IÉSEG School of Management ( email )
3, rue de la Digue
Lille, 59000
HOME PAGE: http://goo.gl/2mUIP
University of Bologna - Rimini Center for Economic Analysis (RCEA) ( email )
Via Patara, 3
Rimini (RN), RN 47900
Ramazan Gencay
Simon Fraser University ( email )
Department of Economics
8888 University Drive
Burnaby, British Columbia V5A 1S6
Feedback to SSRN

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