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Markov Chain Monte Carlo

In: Handbook of Financial Time Series

Author

Listed:
  • Michael Johannes

    (Columbia University, Graduate School of Business)

  • Nicholas Polson

    (University of Chicago, Graduate School of Business,)

Abstract

This chapter provides an overview of Markov Chain Monte Carlo (MCMC) methods. MCMC methods provide samples from high-dimensional distributions that commonly arise in Bayesian inference problems. We review the theoretical underpinnings used to construct the algorithms, the Metropolis-Hastings algorithm, the Gibbs sampler, Markov Chain convergence, and provide a number of examples in financial econometrics.

Suggested Citation

  • Michael Johannes & Nicholas Polson, 2009. "Markov Chain Monte Carlo," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 43, pages 1001-1013, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-71297-8_43
    DOI: 10.1007/978-3-540-71297-8_43
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