Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients

Xuerong Mao, Lukasz Szpruch

Research output: Contribution to journalArticle

75 Citations (Scopus)
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Abstract

We are interested in the strong convergence and almost sure stability of Euler-Maruyama (EM) type approximations to the solutions of stochastic differential equations (SDEs) with non-linear and non-Lipschitzian coefficients. Motivation comes from finance and biology where many widely applied models do not satisfy the standard assumptions required for the strong convergence. In addition we examine the globally almost surely asymptotic stability in this non-linear setting for EM type schemes. In particular, we present a stochastic counterpart of the discrete LaSalle principle from which we deduce stability properties for numerical methods.
Original languageEnglish
Pages (from-to)14-28
Number of pages15
JournalJournal of Computational and Applied Mathematics
Volume238
DOIs
Publication statusPublished - Jan 2013

Keywords

  • super-linear growth
  • stochastic differential equation
  • strong convergence
  • backward Euler–Maruyama scheme
  • LaSalle principle
  • almost sure stability

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