Ghost Calibration and Pricing Barrier Options and CDS in Spectrally One-Sided L'evy Models: The Parabolic Laplace Inversion Method

M. Boyarchenko, S. Levendorskii
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引用次数: 8

Abstract

Recently, the advantages of conformal deformations of the contours of integration in pricing formulas were demonstrated in the context of wide classes of Levy models and the Heston model. In the present paper, we construct efficient conformal deformations of the contours of integration in the pricing formulas for barrier options and CDS in the setting of spectrally one-sided Levy models, taking advantage of Rogers's trick [ J. Appl. Prob. , 2000, 37 , 1173-1180] that greatly simplifies calculation of the Wiener-Hopf factors. We extend the trick to wide classes of Levy processes of infinite variation with zero diffusion component. In the resulting formulas (both in the finite variation and the infinite variation cases), we make quasi-parabolic deformations as in Boyarchenko and Levendorskiĭ [ Int. J. Theor. Appl. Finance , 2013, 16 (3), 1350011], which greatly increase the rate of convergence of the integrals. We demonstrate that in many cases the proposed method is more accurate than the standard realization of Laplace inversion. We also exhibit examples in which the standard realization is so unstable that it cannot be used for any choice of the error control parameters. This may lead to a ghost calibration : a situation where a parameter set of a model is declared to be a 'good fit' to the data only because the errors of calibration and of the numerical method used for pricing (almost) cancel each other out.
光谱单侧L'evy模型中的幽灵校准和定价障碍期权和CDS:抛物拉普拉斯反演方法
最近,在广泛的Levy模型和Heston模型的背景下,证明了整合轮廓的共形变形在定价公式中的优势。在本文中,我们利用罗杰斯的技巧,构造了频谱单侧Levy模型下障碍期权和CDS定价公式中积分轮廓的有效保形变形[J. Appl]。概率。[j] .数学学报,2000,37,1173-1180],极大地简化了Wiener-Hopf因子的计算。我们将这一技巧推广到具有零扩散分量的无限变化的Levy过程的广泛类别。在得到的公式中(在有限变分和无限变分的情况下),我们使拟抛物型变形如Boyarchenko和levendorski [Int.]j理论的。达成。金融学,2013,16(3),1350011],大大提高了积分的收敛速度。我们证明,在许多情况下,所提出的方法比拉普拉斯反演的标准实现更准确。我们还展示了一些例子,其中标准实现是如此不稳定,以至于它不能用于任何错误控制参数的选择。这可能会导致幽灵校准:一种情况下,一个模型的参数集被宣布为数据的“良好拟合”,只是因为校准和用于定价的数值方法的误差(几乎)相互抵消。
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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