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Numerically pricing American options under the generalized mixed fractional Brownian motion model

机译:在广义混合分数布朗运动模型下对美式期权进行数值定价

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In this paper, we introduce a robust numerical method, based on the upwind scheme, for the pricing of American puts under the generalized mixed fractional Brownian motion (GMFBM) model. By using portfolio analysis and applying the Wick-Ito formula, a partial differential equation (PDE) governing the prices of vanilla options under the GMFBM is successfully derived for the first time. Based on this, we formulate the pricing of American puts under the current model as a linear complementarity problem (LCP). Unlike the classical Black-Scholes (B-S) model or the generalized B-S model discussed in Cen and Le (2011), the newly obtained LCP under the GMFBM model is difficult to be solved accurately because of the numerical instability which results from the degeneration of the governing PDE as time approaches zero. To overcome this difficulty, a numerical approach based on the upwind scheme is adopted. It is shown that the coefficient matrix of the current method is an M-matrix, which ensures its stability in the maximum-norm sense. Remarkably, we have managed to provide a sharp theoretic error estimate for the current method, which is further verified numerically. The results of various numerical experiments also suggest that this new approach is quite accurate, and can be easily extended to price other types of financial derivatives with an American-style exercise feature under the GMFBM model. (C) 2016 Elsevier B.V. All rights reserved.
机译:在本文中,我们介绍了一种基于逆风方案的鲁棒数值方法,用于在广义混合分数布朗运动(GMFBM)模型下对美国认沽期权进行定价。通过使用投资组合分析并应用Wick-Ito公式,首次成功导出了控制GMFBM下香草期权价格的偏微分方程(PDE)。基于此,我们将当前模型下的美国看跌期权定价公式化为线性互补问题(LCP)。与经典的Black-Scholes(BS)模型或Cen和Le(2011)中讨论的广义BS模型不同,在GMFBM模型下,新获得的LCP难以精确求解,这是由于模型退化导致的数值不稳定。在时间接近零时控制PDE。为了克服这一困难,采用了基于迎风方案的数值方法。结果表明,当前方法的系数矩阵是一个M矩阵,可以保证其在最大范数意义上的稳定性。值得注意的是,我们设法为当前方法提供了一个清晰的理论误差估计,并在数值上进行了进一步验证。各种数值实验的结果也表明,这种新方法非常准确,并且可以容易地扩展为使用GMFBM模型下具有美式行使功能的其他类型的金融衍生产品定价。 (C)2016 Elsevier B.V.保留所有权利。

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