论文标题
神经最佳停止边界
Neural Optimal Stopping Boundary
论文作者
论文摘要
开发了一种基于深人造神经网络和经验风险最小化的方法，以计算最佳停止中停止和延续区域的边界。该算法将停止边界作为函数的图表进行参数化，并根据模糊边界引入放松的停止规则，以促进有效优化。通过这种方法分析了几种金融工具，有些是高度的，证明了其有效性。在自然的结构假设下，也证明了停止边界的存在。
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.
