摘要
期权定价理论源于影响期权价格的变量和期权价格之间的非线性关系,传统的Black-Scholes期权定价公式过于严格的假设削弱了该公式在现实中的适用性,使其在理论与应用上均存在缺陷。因此,能够以任意精度近似复杂非线性系统的神经网络运用于期权定价。分别利用BP神经网络和Black-Scholes期权定价公式对S&P 500指数看跌期权进行定价,实证结果表明BP神经网络的定价结果要优于Black-Scholes定价公式。
Option- pricing theory typically derives nonlinear relations between the variable determining it and an option price, however, the traditional Black - Scholes option pricing formula has such strict supposition that weakened its use in the reality application. Therefore, neural networks may be well suited for this purpose due to their ability to approximate complex nonlinear relations to an arbitrary degree of accuracy. The aim of the article is to price the S＆P 500 index put option by the back- propagation networks. The results show that the BP networks outweigh the traditional Black Scholes option pricing formula.
出处
《统计与信息论坛》
CSSCI
2008年第11期40-43,共4页
Journal of Statistics and Information
