Artificial Neural Network Enhanced Parametric Option Pricing
AbstractIn this paper we explore ways that alleviate problems of nonparametric (artificial neural networks) and parametric option pricing models by combining the two. The resulting enhanced network model is compared to standard artificial neural networks and to parametric models with several historical and implied parameters. Empirical results using S\&P 500 index call options strongly support our approach.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 118.
Date of creation: 04 Jul 2006
Date of revision:
Option pricing; implied volatilities; implied parameters; artificial neural networks; optimization;
Find related papers by JEL classification:
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-15 (All new papers)
- NEP-CFN-2006-07-15 (Corporate Finance)
- NEP-CMP-2006-07-15 (Computational Economics)
- NEP-ETS-2006-07-15 (Econometric Time Series)
- NEP-FIN-2006-07-15 (Finance)
- NEP-MIC-2006-07-15 (Microeconomics)
- NEP-SOC-2006-07-15 (Social Norms & Social Capital)
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