神经最优停止边界
Neural Optimal Stopping Boundary
AI总结:
本文提出基于深度神经网络和经验风险最小化的方法计算最优停止边界,通过参数化边界并引入模糊松弛规则优化,验证了其在高维金融工具中的有效性并证明了边界存在性。
摘要:
本文开发了一种基于深度人工神经网络和经验风险最小化的方法,用于计算最优停止中分离停止区域与延续区域的边界。该算法将停止边界参数化为函数的图像,并引入基于模糊边界的松弛停止规则以促进高效优化。通过该方法分析了多种金融工具(部分处于高维情形),证明了其有效性。在自然结构假设下,也证明了停止边界的存在性。
英文摘要:
A method based on deep artificial neural networks and empirical risk minimization is developed to calculate the boundary separating the stopping and continuation regions in optimal stopping. The algorithm parameterizes the stopping boundary as the graph of a function and introduces relaxed stopping rules based on fuzzy boundaries to facilitate efficient optimization. Several financial instruments, some in high dimensions, are analyzed through this method, demonstrating its effectiveness. The existence of the stopping boundary is also proved under natural structural assumptions.
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。