论文标题
有效的稀有事件模拟，以通过无限活动定期改变莱维过程中的多个跳跃事件
Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Lévy Processes with Infinite Activities
论文作者
论文摘要
在本文中，我们解决了针对无限活动的重尾lévy过程的稀有事实模拟的问题。我们提出了一种非常有效的重要性采样算法，该算法基于样本路径的大偏差，用于重尾lévy工艺，莱维过程的极端突破性近似以及随机的蒙特卡洛方案。提出的重要性采样算法可以应用于一系列的lévy过程，并且与我们的数值实验中的粗糙蒙特卡洛方法相比，效率显着提高。
In this paper we address the problem of rare-event simulation for heavy-tailed Lévy processes with infinite activities. We propose a strongly efficient importance sampling algorithm that builds upon the sample path large deviations for heavy-tailed Lévy processes, stick-breaking approximation of extrema of Lévy processes, and the randomized debiasing Monte Carlo scheme. The proposed importance sampling algorithm can be applied to a broad class of Lévy processes and exhibits significant improvements in efficiency when compared to crude Monte-Carlo method in our numerical experiments.
