A Hybrid Option Pricing Model Using a Neural Network for Estimating Volatility
The Black-Scholes (BS) 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 markets. This paper applies a hybrid neural network which preprocesses financial input data for improving the estimation of option market prices. This model is comprised of two parts. The first part is a neural network developed to estimate volatility. The second part is an additional neural network developed to value the difference between the BS model results and the actual market option prices. The resulting option price is then a summation between the BS model and the network response. The hybrid system with a neural network for estimating volatility provides better performance in terms of pricing accuracy than either the BS model with historical volatility (HV), or the BS model with volatility valued by the neural network.
S. Amornwattana et al., "A Hybrid Option Pricing Model Using a Neural Network for Estimating Volatility," International Journal of General Systems, Taylor & Francis, Jan 2007.
The definitive version is available at https://doi.org/10.1080/03081070701210303
Engineering Management and Systems Engineering
Keywords and Phrases
Black-Scholes; Hybrid Model; Non-Linear Systems; Option Pricing; Neural networks (Computer science)
International Standard Serial Number (ISSN)
Article - Journal
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