基于神经网络的金融随机控制和偏微分方程算法
Neural networks-based algorithms for stochastic control and PDEs in finance
摘要：
本文提出了机器学习技术和基于深度强化学习的算法，用于有效解决金融工程中投资决策和衍生品定价中出现的非线性偏微分方程和动态最优化问题。我们调查最近的结果在文献中，提出新的发展，特别是在完全非线性的情况下，并比较不同的方案说明各种金融应用的数值试验。最后，我们强调了一些未来的研究方向。
英文摘要：
This paper presents machine learning techniques and deep reinforcement learningbased algorithms for the efficient resolution of nonlinear partial differential equations and dynamic optimization problems arising in investment decisions and derivative pricing in financial engineering. We survey recent results in the literature, present new developments, notably in the fully nonlinear case, and compare the different schemes illustrated by numerical tests on various financial applications. We conclude by highlighting some future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
