Option Pricing with Modular Neural Networks
This 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)
|Date of creation:||Jan 2009|
|Date of revision:||Jan 2009|
|Contact details of provider:|| Postal: |
Web page: http://www.rcfea.orgEmail:
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:rim:rimwps:32_09. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco Savioli)
If references are entirely missing, you can add them using this form.