论文标题
财务中的深层随机优化
Deep Stochastic Optimization in Finance
论文作者
论文摘要
本文概述并通过风格化的示例评估了一种新颖且高效的计算技术。经验风险最小化（ERM）和神经网络是这种方法的关键。强大的开源优化库可以有效地实现此算法，从而使其在高维结构中可行。与美国和百慕大选项有关的自由边界问题展示了特定应用程序可能面临的功率和潜在困难。在简化的默顿类型问题中研究了训练数据大小的影响。经典选项对冲问题体现了市场发生器或大量仿真的需求。
This paper outlines, and through stylized examples evaluates a novel and highly effective computational technique in quantitative finance. Empirical Risk Minimization (ERM) and neural networks are key to this approach. Powerful open source optimization libraries allow for efficient implementations of this algorithm making it viable in high-dimensional structures. The free-boundary problems related to American and Bermudan options showcase both the power and the potential difficulties that specific applications may face. The impact of the size of the training data is studied in a simplified Merton type problem. The classical option hedging problem exemplifies the need of market generators or large number of simulations.
