Capturing the volatility smile of options on high-tech stocks—A combined GARCH-neural network approach
A slight modification of the standard GARCH equation results in a good modeling of historical volatility. Using this generated GARCH volatility together with the inputs: spot price divided by strike, time to maturity, and interest rate, a generated Neural Network results in significantly better pricing performance than the Black Scholes model. A single Neural Network for each individual high-tech stock is able to adapt to the market inherent volatility distortion. A single Network for all tested high-tech stocks also results in significantly better pricing performance than the Black-Scholes model. Copyright Academy of Economics and Finance 2001
Year of publication: |
2001
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Authors: | Meissner, Gunter ; Kawano, Noriko |
Published in: |
Journal of Economics and Finance. - Springer, ISSN 1055-0925. - Vol. 25.2001, 3, p. 276-292
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Publisher: |
Springer |
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