美式期权定价:一个具有智能格搜索的加速格模型

Qianru Shang, Brian Byrne
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引用次数: 2

摘要

作者在文献中介绍了一种智能格搜索算法,以有效地定位美式选项的最佳练习边界。格模型可以通过结合智能格搜索、截断和动态记忆来加速。我们将计算运行时间从超过18分钟减少到不到3秒,以估计15000步的二项式树,其中获得的结果与广受好评的文献一致。德尔塔和隐含波动率也可以相对于标准模型加速。一般来说,格点估计被认为是缓慢的,对于评估大额期权或及时重新平衡风险中性投资组合来说是不现实的。我们的技术转换标准的二项式树,并使它们至少与常用的分析公式持平。更重要的是,智能格搜索可以进行调整,以在不同的步长下达到不同的精度水平,而传统的分析公式则不那么灵活。主题:期权、衍生品
本文章由计算机程序翻译,如有差异,请以英文原文为准。
American Option Pricing: An Accelerated Lattice Model with Intelligent Lattice Search
The authors introduce to the literature an intelligent lattice search algorithm to efficiently locate the optimal exercise boundary for American options. Lattice models can be accelerated by incorporating intelligent lattice search, truncation, and dynamic memory. We reduce computational runtime from over 18 minutes down to less than 3 seconds to estimate a 15,000-step binomial tree where the results obtained are consistent with a widely acclaimed literature. Delta and implied volatility can also be accelerated relative to standard models. Lattice estimation, in general, is considered to be slow and not practical for valuing large books of options or for promptly rebalancing a risk-neutral portfolio. Our technique transforms standard binomial trees and renders them to be at least on par with commonly used analytical formulae. More importantly, intelligent lattice search can be tweaked to reach varying levels of accuracy with different step size, while conventional analytical formulae are less flexible. TOPICS: Options, derivatives
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来源期刊
自引率
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发文量
11
审稿时长
24 weeks
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