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arXiv 2407.17151q-fin.CPcs.NAmath.NA
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对数Heston过程的高阶逼近与模拟方案

High order approximations and simulation schemes for the log-Heston process

Aurélien Alfonsi, Edoardo Lombardo

AI总结:

本研究基于Alfonsi和Bally(2021)的方法,结合Ninomiya-Victoir方案或二阶精确波动率采样方案,实现了Heston模型任意阶弱逼近,经金融算例验证收敛阶,且在多类扩展模型中展现应用潜力。

摘要:

我们提出了Heston模型任意阶的弱逼近方案,这些方案通过使用Alfonsi和Bally(2021)提出的方法得到。该方法的核心是结合在不同随机网格上计算的逼近方案,以提高收敛阶数。我们将该方法与Ninomiya-Victoir方案(2008)或一种可精确采样波动率分量的二阶方案结合使用,并严格证明了由此可实现任意阶的收敛阶数。我们通过金融实例给出了数值验证,证实了理论上的收敛阶数。我们还展示了多因子/粗糙Heston模型的可观数值结果,并指出了该方法在其他模型中的应用,包括Bates模型和双Heston模型。

英文摘要:

We present weak approximations schemes of any order for the Heston model that are obtained by using the method developed by Alfonsi and Bally (2021). This method consists in combining approximation schemes calculated on different random grids to increase the order of convergence. We apply this method with either the Ninomiya-Victoir scheme (2008) or a second-order scheme that samples exactly the volatility component, and we show rigorously that we can achieve then any order of convergence. We give numerical illustrations on financial examples that validate the theoretical order of convergence. We also present promising numerical results for the multifactor/rough Heston model and hint at applications to other models, including the Bates model and the double Heston model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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