Option Pricing with Modular Neural Networks
AbstractThis paper investigates 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. When compared to an array of parametric and non-parametric models, the MNN method consistently exerts superior out-of-sample pricing performance. We conclude that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint)
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Bibliographic InfoPaper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 32_09.
Date of creation: Jan 2009
Date of revision: Jan 2009
Option Pricing; Modular Neural Networks; Non-parametric Methods;
Find related papers by JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
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- Nikola Gradojevic & Dragan Kukolj & Ramazan Gencay, 2011. "Clustering and Classification in Option Pricing," Review of Economic Analysis, Rimini Centre for Economic Analysis, vol. 3(2), pages 109-128, October.
- Dragan Kukolj & Nikola Gradojevic & Camillo Lento, 2012. "Improving Non-Parametric Option Pricing during the Financial Crisis," Working Paper Series 35_12, The Rimini Centre for Economic Analysis.
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