Numerical method for stationary distribution of stochastic differential equations with Markovian switching

X. Mao, C. Yuan, G. Yin

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

In principle, once the existence of the stationary distribution of a stochastic differential equation with Markovian switching is assured, we may compute it by solving the associated system of the coupled Kolmogorov-Fokker-Planck equations. However, this is nontrivial in practice. As a viable alternative, we use the Euler-Maruyama scheme to obtain the stationary distribution in this paper.
LanguageEnglish
Pages1-27
Number of pages26
JournalJournal of Computational and Applied Mathematics
Volume174
Issue number1
DOIs
Publication statusPublished - Feb 2005

Fingerprint

Markovian Switching
Fokker Planck equation
Stationary Distribution
Stochastic Equations
Numerical methods
Differential equations
Numerical Methods
Differential equation
Kolmogorov Equation
Fokker-Planck Equation
Euler
Alternatives

Keywords

  • Brownian motion
  • stationary distribution
  • Lipschitz condition
  • Markov chain
  • stochastic differential equations
  • Euler-Maruyama methods
  • weak convergence to stationary measures

Cite this

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Numerical method for stationary distribution of stochastic differential equations with Markovian switching. / Mao, X.; Yuan, C.; Yin, G.

In: Journal of Computational and Applied Mathematics, Vol. 174, No. 1, 02.2005, p. 1-27.

Research output: Contribution to journalArticle

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KW - Brownian motion

KW - stationary distribution

KW - Lipschitz condition

KW - Markov chain

KW - stochastic differential equations

KW - Euler-Maruyama methods

KW - weak convergence to stationary measures

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