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An Efficient Parallel Simulation Method for Posterior Inference on Paths of Markov Processes

Author

Listed:
  • Matthias Held

    (Faculty of Finance, WHU - Otto Beisheim School of Management)

  • Marcel Omachel

    (Faculty of Finance, WHU - Otto Beisheim School of Management)

Abstract

In this note, we propose a method for efficient simulation of paths of latent Markovian state processes in a Markov Chain Monte Carlo setting. Our method harnesses available parallel computing power by breaking the sequential nature of commonly encountered state simulation routines. We offer a worked example that highlights the computational merits of our approach.

Suggested Citation

  • Matthias Held & Marcel Omachel, 2014. "An Efficient Parallel Simulation Method for Posterior Inference on Paths of Markov Processes," FEMM Working Papers 140010, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    • Handle: RePEc:mag:wpaper:140010
    as

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    References listed on IDEAS

    as
    1. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    2. Eraker, Bjorn, 2001. "MCMC Analysis of Diffusion Models with Application to Finance," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 177-191, April.
    3. Sylvia Frühwirth‐Schnatter, 1994. "Data Augmentation And Dynamic Linear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(2), pages 183-202, March.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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