Rough Heston: The SINC way

Fabio Baschetti, G. Bormetti, S. Romagnoli, P. Rossi
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引用次数: 2

Abstract

The goal of this paper is to investigate the method outlined by one of us (PR) in Cherubini et al. (2009) to compute option prices. We named it the SINC approach. While the COS method by Fang and Osterlee (2009) leverages the Fourier-cosine expansion of truncated densities, the SINC approach builds on the Shannon Sampling Theorem revisited for functions with bounded support. We provide several important results which were missing in the early derivation: i) a rigorous proof of the converge of the SINC formula to the correct option price when the support growths and the number of Fourier frequencies increases; ii) ready to implement formulas for put, Cash-or-Nothing, and Asset-or-Nothing options; iii) a systematic comparison with the COS formula in several settings; iv) a numerical challenge against alternative Fast Fourier specifications, such as Carr and Madan (1999) and Lewis (2000); v) an extensive pricing exercise under the rough Heston model of Jaisson and Rosenbaum (2015); vi) formulas to evaluate numerically the moments of a truncated density. The advantages of the SINC approach are numerous. When compared to benchmark methodologies, SINC provides the most accurate and fast pricing computation. The method naturally lends itself to price all options in a smile concurrently by means of Fast Fourier techniques, boosting fast calibration. Pricing requires to resort only to odd moments in the Fourier space.
Rough Heston: SINC的方式
本文的目的是研究我们(PR)在Cherubini等人(2009)中概述的计算期权价格的方法。我们称之为SINC方法。Fang和Osterlee(2009)的COS方法利用截断密度的傅立叶-余弦展开,而SINC方法建立在Shannon抽样定理的基础上,对有界支持的函数进行了重新审视。我们提供了在早期推导中缺少的几个重要结果:i)严格证明了当支持度增加和傅立叶频率数量增加时,SINC公式收敛于正确的期权价格;ii)准备实施看跌期权、现金或无现金期权和资产或无资产期权的公式;iii)在若干情况下与COS公式进行系统比较;iv)对替代快速傅里叶规范的数值挑战,如Carr和Madan(1999)和Lewis (2000);v)在Jaisson和Rosenbaum(2015)的粗略赫斯顿模型下进行广泛的定价;Vi)计算截断密度矩的数值公式。SINC方法的优点很多。与基准方法相比,SINC提供了最准确、最快速的定价计算。通过快速傅立叶技术,该方法自然可以同时为微笑中的所有选项定价,从而提高快速校准。定价只需要利用傅里叶空间中的奇矩。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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