英文标题：
《Deep Curve-dependent PDEs for affine rough volatility》
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作者：
Antoine Jacquier and Mugad Oumgari
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最新提交年份：
2020
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英文摘要：
We introduce a new deep-learning based algorithm to evaluate options in affine rough stochastic volatility models. We show that the pricing function is the solution to a curve-dependent PDE (CPDE), depending on forward curves rather than the whole path of the process, for which we develop a numerical scheme based on deep learning techniques. Numerical simulations suggest that the latter is extremely efficient, and provides a good alternative to classical Monte Carlo simulations.
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中文摘要：
我们引入了一种新的基于深度学习的算法来评估仿射粗糙随机波动率模型中的期权。我们证明了定价函数是依赖于曲线的偏微分方程（CPDE）的解，它依赖于前向曲线而不是整个过程的路径，为此我们开发了一个基于深度学习技术的数值方案。数值模拟表明，后者非常有效，为经典蒙特卡罗模拟提供了一个很好的替代方案。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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