论文标题
希尔伯特空间中神经网络向前流的定价选项
Pricing options on flow forwards by neural networks in Hilbert space
论文作者
论文摘要
我们通过应用无限维神经网络提出了一种新的方法，用于对流向前进的定价选项。我们将定价问题作为一个优化问题在正面真实线上的实现函数的希尔伯特空间中的优化问题，这是术语结构动力学的状态空间。通过促进旨在近似状态空间上连续功能的新型前馈神经网络体系结构来解决此优化问题。拟议的神经网是建立在希尔伯特空间的基础上的。我们提供了一项广泛的案例研究，显示出卓越的数值效率，其性能优于经典神经网的表现，该神经网训练了术语结构曲线。
We propose a new methodology for pricing options on flow forwards by applying infinite-dimensional neural networks. We recast the pricing problem as an optimization problem in a Hilbert space of real-valued function on the positive real line, which is the state space for the term structure dynamics. This optimization problem is solved by facilitating a novel feedforward neural network architecture designed for approximating continuous functions on the state space. The proposed neural net is built upon the basis of the Hilbert space. We provide an extensive case study that shows excellent numerical efficiency, with superior performance over that of a classical neural net trained on sampling the term structure curves.
