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A Markov-Chain Sampling Algorithm for GARCH Models

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  • Nakatsuma Teruo

    () (Institute of Economic Research Hitotsubashi University)

Abstract

This paper describes a GAUSS program of a Markov-chain sampling algorithm for GARCH models proposed by Nakatsuma (1998). This algorithm allows us to generate Monte Carlo samples of parameters in a GARCH model from their joint posterior distribution. The samples obtained by this algorithm are used for Bayesian analysis of the GARCH model. As numerical examples, GARCH models of simulated data and of weekly foreign exchange rate series are estimated and analyzed.

Suggested Citation

  • Nakatsuma Teruo, 1998. "A Markov-Chain Sampling Algorithm for GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(2), pages 1-13, July.
  • Handle: RePEc:bpj:sndecm:v:3:y:1998:i:2:n:al1
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    References listed on IDEAS

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    3. Simon M. Potter, 1993. "A Nonlinear Approach to U.S. GNP," UCLA Economics Working Papers 693, UCLA Department of Economics.
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    5. Philip Rothman, "undated". "Forecasting Asymmetric Unemployment Rates," Working Papers 9618, East Carolina University, Department of Economics.
    6. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, pages 164-168.
    7. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-125, April-Jun.
    8. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, pages 119-147.
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    Cited by:

    1. Sarantis Tsiaplias, 2007. "A Metropolis-in-Gibbs Sampler for Estimating Equity Market Factors," Melbourne Institute Working Paper Series wp2007n18, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    2. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    3. Roberto Casarin & Monica Billio & Anthony Osuntuyi, 2014. "Markov Switching GARCH models for Bayesian Hedging on Energy Futures Markets," Working Papers 2014:07, Department of Economics, University of Venice "Ca' Foscari".
    4. Rodríguez Bernal, M. T. & Marín Díazaraque, Juan Miguel & Romero, Eva, 2013. "Data cloning estimation of GARCH and COGARCH models," DES - Working Papers. Statistics and Econometrics. WS ws132723, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2016. "Efficient Gibbs sampling for Markov switching GARCH models," Computational Statistics & Data Analysis, Elsevier, pages 37-57.
    6. David Ardia & Lennart F. Hoogerheide, 2010. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations," Tinbergen Institute Discussion Papers 10-045/4, Tinbergen Institute.

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