Neural network design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Computation of Huber's M-estimates for a block-angular regression problem
Computational Statistics & Data Analysis
Risk-neutral density extraction from option prices: improved pricing with mixture density networks
IEEE Transactions on Neural Networks
Computational intelligence for evolving trading rules
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
An Investigation into the Use of Intelligent Systems for Currency Trading
Computational Economics
Pricing And Hedging Short Sterling Options Using Neural Networks
International Journal of Intelligent Systems in Accounting and Finance Management
An Abductive-Reasoning Guide for Finance Practitioners
Computational Economics
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The option pricing ability of Robust Artificial Neural Networks optimized with the Huber function is compared against those optimized with Least Squares. Comparison is in respect to pricing European call options on the S&P 500 using daily data for the period April 1998 to August 2001. The analysis is augmented with the use of several historical and implied volatility measures. Implied volatilities are the overall average, and the average per maturity. Beyond the standard neural networks, hybrid networks that directly incorporate information from the parametric model are included in the analysis. It is shown that the artificial neural network models with the use of the Huber function outperform the ones optimized with least squares.