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摘要翻译：
本文给出了由L\'evy过程或一般半鞅驱动的随机微分方程强逼近的一种方法。我们的方法的主要成分是对SDE的扰动和得到的参数化曲线的泰勒展开。我们应用该方法建立了LIBOR市场模型的强逼近格式。特别是，我们推导了快速和精确的算法，在LIBOR模型中的衍生品估值比模拟完全SDE更容易。L\'Evy LIBOR模型的一个数值例子说明了我们的方法。
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英文标题：
《Strong Taylor approximation of stochastic differential equations and
application to the L\'evy LIBOR model》
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作者：
Antonis Papapantoleon, Maria Siopacha
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最新提交年份：
2010
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分类信息：
一级分类：Mathematics 数学
二级分类：Probability 概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
In this article we develop a method for the strong approximation of stochastic differential equations (SDEs) driven by L\'evy processes or general semimartingales. The main ingredients of our method is the perturbation of the SDE and the Taylor expansion of the resulting parameterized curve. We apply this method to develop strong approximation schemes for LIBOR market models. In particular, we derive fast and precise algorithms for the valuation of derivatives in LIBOR models which are more tractable than the simulation of the full SDE. A numerical example for the L\'evy LIBOR model illustrates our method.
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PDF链接：
https://arxiv.org/pdf/0906.5581
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