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Title
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An adjoint method for the exact calibration of stochastic local volatility models
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Author
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Abstract
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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. |
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Language
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English
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Source (journal)
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Journal of Computational Science
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Publication
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2018
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ISSN
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1877-7503
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DOI
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10.1016/J.JOCS.2017.02.004
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Volume/pages
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24
(2018)
, p. 182-194
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ISI
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000426412200019
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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