具有无限活动的重尾lsamvy过程中多个跳跃事件的稀疏事件模拟

Xingyu Wang, C. Rhee
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引用次数: 0

摘要

本文研究了具有无限活动的重尾lsamvy过程的稀疏事件模拟问题。我们提出了一种高效的重要抽样算法,该算法建立在重尾lsamuvy过程的样本路径大偏差、lsamuvy过程极值的断棒近似和随机去偏蒙特卡罗方案的基础上。本文提出的重要抽样算法可以应用于广泛的lsamvy过程,并且在数值实验中与原始的蒙特卡罗方法相比,在效率上有了显著的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rare-Event Simulation for Multiple Jump Events in Heavy-Tailed Lévy Processes with Infinite Activities
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.
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