Pricing Options with Futures-Style Margining: A Genetic Adaptive Neural Network Approach Book

This book examines the applicability of a relatively new and powerful tool, genetic adaptive neural networks, to the field of option valuation. A genetic adaptive neural network model is developed to price option contracts with futures-style margining. This model is capable of estimating complex, non-linear relationships without having prior knowledge of the specific nature of the relationships. Traditional option pricing models require that the researcher or practitioner specify the distribution of the underlying asset. In addition, the methodology is able to easily accommodate additional inputs(something that cannot be preformed with existing models.
Since 1973, options on stock have been traded on organized exchanges in the United States. An option on a stock gives the option owner the right to buy or sell the stock for a pre-set price.. Since the introduction of stock options, the options market has experienced tremendous growth and has spawned even more exotic types of derivative securities. Obviously, valuing these securities is an issue of great importance to investors and hedgers in the financial marketplace. Existing pricing models produce systematic pricing errors and new models have to be developed for options with differing characteristics. The genetic adaptive neural network is found to provide more accurate valuation than a traditional option pricing model when applied to the 3-month Eurodollar futures-option contract traded on the London International Financial Futures and Options Exchange.

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Uses the Genetic Adaptive Neural Networks (GANNs) model to develop a method for pricing futures options with futures-style margining. Establishes the GANNs ability to approximate a pre-specified option pricing function through simulation, develops an artificial neural network that will price futures options with futures-style margining using real data, and examines the effects of incorporating additional economic data into the pricing of futures options when using GANNs. After a literature review, chapters present the methodology and simulation and real data sets, and discuss previous neural network applications for finance. Annotation c. Book News, Inc., Portland, OR (booknews.com)