Scholarlink

In the chapter, we apply the weak approximation method introduced in the previous chapter for solving a parabolic partial differential equation and a backward dynamic programming problem. In particular, we solve nested conditional expectations appearing in Bermudan option pricing problem by deep learning-based least squares regression combined with the weak approximation under high-dimensional diffusion settings.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application: Deep Learning-Based Weak Approximation

  • 章节

  • 发表日期：

    2025年

查看全文
  • Akihiko Takahashi,
  • Toshihiro Yamada

摘要

In the chapter, we apply the weak approximation method introduced in the previous chapter for solving a parabolic partial differential equation and a backward dynamic programming problem. In particular, we solve nested conditional expectations appearing in Bermudan option pricing problem by deep learning-based least squares regression combined with the weak approximation under high-dimensional diffusion settings.