BAYESIAN ANALYSIS OF A MARKOV SWITCHING STOCHASTIC VOLATILITY MODEL

Access this Article

Search this Article

Author(s)

Abstract

This article analyzes a Markov switching stochastic volatility (MSSV) model to accommodate the shift in the mean of log-volatility. Since it is difficult to estimate the parameters in this model based on the maximum likelihood method, a Bayesian Markov-chain Monte Carlo (MCMC) approach is adopted. A particle filter for the MSSV model, which is used for model comparison and diagnostics, is constructed. The estimation result, based on weekly returns of the TOPIX, confirms the finding by previous researchers that the estimate of the persistence parameter drops and the estimate of the error variance rises in the volatility equation of the MSSV model compared to those of the standard SV model. The model comparison provides evidence that the MSSV model is favored over the standard SV model. It is also found that the MSSV model passes the diagnostic tests based on the statistics obtained from the particle filter while the SV model does not.

This article analyzes a Markov switching stochastic volatility (MSSV) model to accommodate the shift in the mean of log-volatility. Since it is difficult to estimate the parameters in this model based on the maximum likelihood method, a Bayesian Markov-chain Monte Carlo (MCMC) approach is adopted. A particle filter for the MSSV model, which is used for model comparison and diagnostics, is constructed. The estimation result, based on weekly returns of the TOPIX, confirms the finding by previous researchers that the estimate of the persistence parameter drops and the estimate of the error variance rises in the volatility equation of the MSSV model compared to those of the standard SV model. The model comparison provides evidence that the MSSV model is favored over the standard SV model. It is also found that the MSSV model passes the diagnostic tests based on the statistics obtained from the particle filter while the SV model does not.

Journal

  • JOURNAL OF THE JAPAN STATISTICAL SOCIETY

    JOURNAL OF THE JAPAN STATISTICAL SOCIETY 35(2), 205-219, 2005-12-01

    THE JAPAN STATISTICAL SOCIETY

References:  34

Cited by:  1

Codes

  • NII Article ID (NAID)
    110003495323
  • NII NACSIS-CAT ID (NCID)
    AA1105098X
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    03895602
  • NDL Article ID
    7966698
  • NDL Source Classification
    ZD43(経済--統計)
  • NDL Call No.
    Z76-A259
  • Data Source
    CJP  CJPref  NDL  NII-ELS  IR  J-STAGE 
Page Top