Strong Predictor-Corrector Euler Methods for Stochastic Differential Equations

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Strong Predictor-Corrector Euler Methods for Stochastic Differential Equations

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Nicola Bruti-Liberati (School of Finance and Economics, University of Technology, Sydney)
Eckhard Platen () (School of Finance and Economics, University of Technology, Sydney)

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Abstract

This paper introduces a new class of numerical schemes for the pathwise approximation of solutions of stochastic differential equations (SDEs). The proposed family of strong predictor-corrector Euler methods are designed to handle scenario simulation of solutions of SDEs. It has the potential to overcome some of the numerical instabilities that are often experienced when using the explicit Euler method. This is of importance, for instance, in finance where martingale dynamics arise for solutions of SDEs with multiplicative diffusion coefficients. Numerical experiments demonstrate the improved asymptotic stability properties of the new symmetric predictor-corrector Euler methods.

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Paper provided by Quantitative Finance Research Centre, University of Technology, Sydney in its series Research Paper Series with number 222.

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Length: 23
Date of creation: 01 Jun 2008
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Handle: RePEc:uts:rpaper:222

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This paper has been announced in the following NEP Reports:

NEP-ALL-2008-07-20 (All new papers)
NEP-CMP-2008-07-20 (Computational Economics)
NEP-ORE-2008-07-20 (Operations Research)

References listed on IDEAS
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Yoshihiro Saito & Taketomo Mitsui, 1993. "Simulation of stochastic differential equations," Annals of the Institute of Statistical Mathematics, Springer, vol. 45(3), pages 419-432, September. [Downloadable!] (restricted)

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Eckhard Platen & Lei Shi, 2008. "On the Numerical Stability of Simulation Methods for SDES," Research Paper Series 234, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]

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