IDEAS home Printed from
   My bibliography  Save this paper

Fitting and comparing stochastic volatility models through Monte Carlo simulations


  • Silvano Bordignon
  • Davide Raggi


Stochastic-variance models are important in describing and forecasting time-varying volatilities of financial time series. The introduction of jump components, in both the returns and the volatility process, improves the fit to the data. The goal of this paper is to examine the effectiveness of Markov Chain Monte Carlo methods in making inferences on different stochastic volatility models. We consider models of the affine-jump diffusion family and the log-variance specification popular in the econometric literature. We conduct inference within various stochastic volatility models, eventually with jumps, using an efficient adaptive Markov-chain Monte-Carlo procedure, thus generalizing solutions previously proposed in the literature. This methodology effects a sensible reduction in the autocorrelation observed in the Markov chain generated by the volatility-process updating scheme. To rank the competing models, we use the Bayes factor. Because there are many latent components (volatility and jumps), this computation is a challenging task. The posterior distribution at a given point is estimated through a sequence of reduced runs of the MCMC algorithm, which is a particular case of a bridge sampling method. The likelihood is computed using an auxiliary particle filter, which is also used to compute the VaR forecasts to provide further validation of the competing models. We show some results for the Standard & Poor's $500$ index series

Suggested Citation

  • Silvano Bordignon & Davide Raggi, 2004. "Fitting and comparing stochastic volatility models through Monte Carlo simulations," Computing in Economics and Finance 2004 219, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:219

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item


    stochastic volatility; jumps processes; MCMC; particle filters; Bayes Factor;

    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


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecf4:219. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.