A neural network model for estimating option prices
A neural network model for estimating option prices
复制标题
用于估计期权价格的神经网络模型
DOI：
10.1007/bf00871937
复制
发表时间：
1993
影响因子：
5.3
通讯作者：
L. Salchenberger
中科院分区：
文献类型：
--
作者：
M. Malliaris;L. Salchenberger
开发了处理财务输入数据的神经网络模型来估计期权收盘时的市场价格。该网络估计收盘价的能力与布莱克-斯科尔斯模型（最广泛使用的期权定价模型）进行了比较。比较表明，在大约一半的检查案例中，神经网络的均方误差小于 Black-Scholes 模型的均方误差。讨论了两种建模方法的差异和相似之处。该神经网络使用与Black-Scholes模型相同的金融数据，不需要分布假设，并从历史数据中学习金融输入数据和期权价格之间的关系。 Black-Scholes 的期权估值均衡模型在价格遵循连续时间路径且瞬时波动性非随机的假设下确定期权价格。
A neural network model that processes financial input data is developed to estimate the market price of options at closing. The network's ability to estimate closing prices is compared to the Black-Scholes model, the most widely used model for the pricing of options. Comparisons reveal that the mean squared error for the neural network is less than that of the Black-Scholes model in about half of the cases examined. The differences and similarities in the two modeling approaches are discussed. The neural network, which uses the same financial data as the Black-Scholes model, requires no distribution assumptions and learns the relationships between the financial input data and the option price from the historical data. The option-valuation equilibrium model of Black-Scholes determines option prices under the assumptions that prices follow a continuous time path and that the instantaneous volatility is nonstochastic.