现实环境下权证定价方式探讨

Exploring the Way of Warrant Pricing in Real Environment

权证是一种特殊期权,B-S模型是迄今为止被认为最精确有效的期权定价模型,但该模型必须建立在许多假设下,而这些假设与现实世界是不尽相符的,使得此模型在实际运作上易产生价格偏差的现象。人工神经网络由于能模拟人脑的思维方式,具有高速计算和学习的特性,使它在预测、评价等方面有很好的应用效果。应用基于BP神经网络的权证定价方法可以缩小对权证价格预测的误差。

The Warrant is one kind of special options, and the B - S model is considered to the most effective options pricing model to the date. This model is established under many suppositions that do not accord with the real world, thus the model may easily result in price deviation in the actual operation. Artificial Neural Network has good application results in the forecast and evaluation, because it can simulate the mentality of the human brain, and has characteristics of high - speed computing and learning. Therefore, the pricing method based on BP Neural Network can reduce the price deviation in the forecast.