Neural optimal stopping boundary
开发了一种基于深度神经网络和风险最小化的方法，用于计算最优停止问题中的停止边界，并通过多个金融工具验证了其有效性。
Abstract 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.