BP神经网络在权证定价中的应用研究

Application Research of BP Neural Network in Warrants Pricing

采用BP神经网络方法,选取2006年4月15日前所有上市欧式权证作为研究对象,建立了权证定价的BP模型。其中,标的股价波动率是影响权证价值的一个重要变量,对所选14只权证的历史波动率和隐含波动率进行了估计,并将其同时作为BP模型的输入参数,以比较其影响。研究结果显示,不论是使用历史波动率还是隐含波动率,BP模型在定价精确度上均优于B-S模型;BP模型使用隐含波动率后,整体降低了与市价的误差。

The paper uses the BP artificial neural network to establish a warrant pricing model, takes a sample of 14 European Warrants listed before April 15, 2006. The volatility of target stock is an important variable that affects the value of warrants. The paper estimates the historical volatility and the implied volatility of 14 warrants, takes them as the input variable of BP model separately to compare their impact. The study shows that, no matter using the fluctuating rate of history or the fluctuating rate of imply , the BP model is superior to the B - S model in the pricing precision, and after using the fluctuating rate of imply, the BP model reduces the deviation with market price on the whole.