Pricing Options with Futures-Style Margining: A Genetic Adaptive Neural Network Approach / Edition 1

by Alan White

Overview

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.

Product Details

ISBN-13:	9781138986688
Publisher:	Taylor & Francis
Publication date:	08/12/2016
Series:	Garland Studies in the Financial Sector of the Economy
Pages:	224
Product dimensions:	5.44(w) x 8.50(h) x (d)

Table of Contents

Chapter I Introduction; Chapter II Literature Review; Chapter III Methodology and Data; Chapter IV Results; Chapter V Conclusions and Suggestions for Future Research;