Gradojevic Nikola, Gençay Ramazan, Kukolj Dragan
Faculty of Business Administration, Lakehead University, Thunder Bay, ON, Canada.
IEEE Trans Neural Netw. 2009 Apr;20(4):626-37. doi: 10.1109/TNN.2008.2011130. Epub 2009 Mar 6.
This paper investigates a nonparametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogeneous of degree one with respect to the underlying index price and the strike price. When compared to an array of parametric and nonparametric models, the MNN method consistently exerts superior out-of-sample pricing performance. We conclude that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint).
本文研究了一种非参数模块化神经网络（MNN）模型，用于对标准普尔500指数欧式看涨期权进行定价。这些模块基于期权的到期时间和实虚值状态。所关注的期权价格函数对于标的指数价格和执行价格而言是一次齐次的。与一系列参数模型和非参数模型相比，MNN方法始终展现出卓越的样本外定价性能。我们得出结论，模块化提高了标准前馈神经网络期权定价模型（有或没有齐次性提示）的泛化特性。
