基于时间分解的CGMY过程序贯抽样-经管之家官网！

英文标题：
《Sequential Sampling for CGMY Processes via Decomposition of their Time
Changes》
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作者：
Chengwei Zhang, Zhiyuan Zhang
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最新提交年份：
2018
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英文摘要：
We present a new and easy-to-implement sequential sampling method for CGMY processes with either finite or infinite variation, exploiting the time change representation of the CGMY model and a decomposition of its time change. We find that the time change can be decomposed into two independent components. While the first component is a \\emph{finite} \\emph{generalized gamma convolution} process whose increments can be sampled by either the exact double CFTP (\"coupling from the past\") method or an approximation scheme with high speed and accuracy, the second component can easily be made arbitrarily small in the $L^1$ sense. Simulation results show that the proposed method is advantageous over two existing methods under a model calibrated to historical option price data.
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中文摘要：
我们利用CGMY模型的时变表示及其时变分解，提出了一种新的、易于实现的有限或无限变化CGMY过程序贯抽样方法。我们发现时间变化可以分解为两个独立的分量。虽然第一个分量是一个\\emph{有限}\\emph{广义gamma卷积}过程，其增量可以通过精确的双CFTP（“过去的耦合”）方法或一个具有高速和准确度的近似方案进行采样，但第二个分量可以很容易地在1美元的意义上变得任意小。仿真结果表明，在基于历史期权价格数据的模型下，该方法优于现有的两种方法。
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分类信息：
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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