12. Markov Chain Monte Carlo Methods with Applications

  1. Ruey S. Tsay

Published Online: 2 AUG 2010

DOI: 10.1002/9780470644560.ch12

Analysis of Financial Time Series, Third Edition, Third Edition

Analysis of Financial Time Series, Third Edition, Third Edition

How to Cite

Tsay, R. S. (2010) Markov Chain Monte Carlo Methods with Applications, in Analysis of Financial Time Series, Third Edition, Third Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470644560.ch12

Author Information

  1. The University of Chicago Booth School of Business, Chicago, IL, USA

Publication History

  1. Published Online: 2 AUG 2010
  2. Published Print: 13 AUG 2010

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470414354

Online ISBN: 9780470644560

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Keywords:

  • Bayesian Inference;
  • Gibbs Sampling;
  • Markov Chain Monte Carlo;
  • Markov switching models;
  • stochastic volatility models

Summary

This chapter introduces the ideas of Markov Chain Monte Carlo (MCMC) methods and data augmentation that are widely applicable in finance. It discusses Bayesian inference through Gibbs sampling and demonstrates various applications of MCMC methods. Rapid developments in the MCMC methodology make it impossible to cover all the new methods. The chapter focuses on issues related to financial econometrics. The demonstrations shown in the chapter represent only a small fraction of all possible applications of the techniques in finance. The chapter reviews the concept of a Markov process. The chapter discusses the MCMC methods for handling missing values and detecting additive outliers. An important financial application of MCMC methods is the estimation of stochastic volatility models.

Controlled Vocabulary Terms

Bayesian Inference; Gibbs Sampling; Markov Chain Monte Carlo