Pricing Options with Futures-Style Margining
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A01=A. Jay White
advanced option valuation techniques
Approximate Option Prices
artificial
Author_A. Jay White
BOPM
Call Option
Call Option Price
Category=KJ
Continuous Dividend
Data Set
Deep In-the Money
Early Exercise Premium
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Eurodollar options
exchange
financial
financial derivatives
Finite Difference Methods
Holdout Sample
Hybrid Neural Network Model
In-the Money Call
Interest Rate Derivative Securities
Interest Rate Future Contract
international
london
London International Financial Futures
machine learning in finance
margin requirements analysis
model
network
neural
neural network modeling
Option Pricing
Option Pricing Analysis
Option Pricing Function
Option Pricing Model
philadelphia
Pricing Biases
Put Option Prices
quantitative finance
Risk Free Rate
Significant Pricing Errors
Small MAE
stock
Wilcoxon Signed Ranks Test Results
Product details
- ISBN 9781138986688
- Weight: 453g
- Dimensions: 138 x 216mm
- Publication Date: 12 Aug 2016
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
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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.
