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Pricing Options with Futures-Style Margining: A Genetic Adaptive Neural Network Approach (Financial Sector of the American Economy)
 
 
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Pricing Options with Futures-Style Margining: A Genetic Adaptive Neural Network Approach (Financial Sector of the American Economy) [Hardcover]

Alan White (Author)

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Book Description

0815333927 978-0815333920 September 28, 2000
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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Inside This Book (learn more)
First Sentence:
In 1973, options on stock became available on an organized exchange when the Chicago Board of Trade created the Chicago Board Options Exchange (CBOE). Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
genetic adaptive neural network, simulation training data, option pricing function, pricing biases, maturity bias, holdout data, interest rate futures options, simulation price, price approximations, early exercise premium, holdout sample, tick move, call option price, instantaneous standard deviation, pricing errors, hidden layer nodes, continuous dividend, strike rate, futures rate, underlying futures contract, option pricing model, bearing securities, training sample
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Sub-sample Description Number of Observations, Journal of Computational Intelligence, Mean Squared Error, Sample Group, Approximation Mean Squared Mean Absolute Error, Results Of Genetic Adaptive Neural Network, Approximation Of The Chen, Function of Futures Rate, Rank Mean, Ranks Z-value, Scott Modified Black-Scholes Formula, Std Dev, Z-value Market Price, Call Option Value, Chicago Board of Trade, London Interbank Offer Rate, Simulation Validation Data Set, Sydney Futures Exchange
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