关键词: 机器学习, 期权定价, 参数模型, 神经网络, 贝叶斯学习

Abstract: This paper designs a new training algorithm which fuses parameter models and nonparameter models. By using the machine learning model to simulate parameter models as prior distribution, optimizing it with Bayesian learning, and dynamically adjusting distribution, this algorithm can avoid the excessive volatility of mode parameters and increase the generation ability. Based on the BS model (Black Scholes option pricing model), the Heston model, and neural network, this paper constructs the fusion modes of BS_BR and HS_BR for option pricing. The result of the empirical study shows that the fusion model can combine the advantages of the two modes, which has not only a better calibration efficiency in the sample, but also a better prediction performance out of the sample.

Key words: machine learning, option pricing, parameter model, neural network, Bayesian learning