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An adjoint method for the exact calibration of stochastic local volatility models

机译:随机局部波动率模型精确校准的一种伴随方法

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This paper deals with the exact calibration of semidiscretized stochastic local volatility (SLV) models to their underlying semidiscretized local volatility (LV) models. Under an SLV model, it is common to approximate the fair value of European-style options by semidiscretizing the backward Kolmogorov equation using finite differences. In the present paper we introduce an adjoint semidiscretization of the corresponding forward Kolmogorov equation. This adjoint semidiscretization is used to obtain an expression for the leverage function in the pertinent SLV model such that the approximated fair values defined by the LV and SLV models are identical for non-path-dependent European-style options. In order to employ this expression, a large non-linear system of ODEs needs to be solved. The actual numerical calibration is performed by combining ADI time stepping with an inner iteration to handle the non linearity. Ample numerical experiments are presented that illustrate the effectiveness of the calibration procedure. (C) 2017 Elsevier B.V. All rights reserved.
机译:本文涉及将半离散随机局部波动率(SLV)模型精确地校准为其基础的半离散局部波动率(LV)模型。在SLV模型下,通常通过使用有限差分对后向Kolmogorov方程进行半离散化来近似欧式期权的公允价值。在本文中,我们介绍了相应的正向Kolmogorov方程的伴随半离散化。该伴随半离散化用于获得相关SLV模型中杠杆函数的表达式,以使LV和SLV模型定义的近似公允值与非路径相关的欧式期权相同。为了采用该表达式,需要解决一个大型的ODE非线性系统。实际的数字校准是通过将ADI时间步长与内部迭代结合以处理非线性来执行的。大量的数值实验表明了校准程序的有效性。 (C)2017 Elsevier B.V.保留所有权利。

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