A Hybrid Option Pricing Model Using a Neural Network for Forecasting Volatility
A great deal of research in the financial domain involves the development of models for pricing financial derivatives, such as options. The Black-Scholes model is the standard approach used for pricing financial options. However, although being theoretically strong, option prices valued by the model often differ from the prices observed in the financial input data for improving the estimation of option market prices. The resulting option price is then a summation between the Black-Scholes model result and the actual market option prices. The Hybrid system with a neural network for estimating volatility provides better performance than either the Black-Scholes model with historical volatility, or the Black-Scholes model with volatility valued by the network.
S. Amornwattana et al., "A Hybrid Option Pricing Model Using a Neural Network for Forecasting Volatility," Intelligent Engineering Systems Through Artificial Neural Networks, American Society of Mechanical Engineers (ASME), Jan 2003.
Engineering Management and Systems Engineering
Keywords and Phrases
Backpropagation; Benchmarking; Brownian Movement; Data Reduction; Digital Signal Processing; Finance; General Regression Neural Network (GRNN); Genetic Algorithms; Mean Absolute Deviation (MAD); Mean Squared Error (MSE); Neural Networks; Option Pricing
Article - Conference proceedings
© 2003 American Society of Mechanical Engineers (ASME), All rights reserved.
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