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A new Technique for Calibrating Stochastic Volatility Models : The Malliavin Gradient Method

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Ewald, Christian-Oliver; Zhang, Aihua

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Abstract We discuss the application of gradient methods to calibrate mean reverting stochastic volatility models. For this we use formulas based on Girsanov transformations as well as a modification of the Bismut-Elworthy formula to compute the derivatives of certain option prices with respect to the parameters of the model by applying Monte Carlo methods. The article presents an extension of the ideas to apply Malliavin calculus methods in the computation of Greek's.
Classification Basic Research, General Concepts and History of Economics; Economic Statistics, Econometrics, Business Informatics
Method theory application
Free Keywords Monte Carlo Methods; Calibration of Stochastic Volatility; Derivative Pricing Models; Option Pricing via Simulation; Computational Finance; Stochastic Volatility; Financial Mathematics; Value at Risk
Document language English
Publication Year 2006
Page/Pages p. 147-158
Journal Quantitative Finance, 6 (2006) 2
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)