Capturing the volatility smile of options on high-tech stocks- A combined GARCH neural network approach

被引：8

作者：

Meissner G. ^{[1
,2
]}

Kawano N. ^{[3
]}

机构：

[1] Department of Finance, Hawaii Pacific University

来源：

Journal of Economics and Finance | 2001年 / 25卷 / 3期

关键词：

Option Price; Radial Basis Function Network; Implied Volatility; Probabilistic Neural Network; Generalize Regression Neural Network;

D O I：

10.1007/BF02745889

中图分类号：

学科分类号：

摘要：

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.

引用

页码：276 / 292

页数：16

共 18 条

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