IDEAS home Printed from https://ideas.repec.org/a/ect/emjrnl/v8y2005i2p235-250.html
   My bibliography  Save this article

Adaptive MCMC methods for inference on affine stochastic volatility models with jumps

Author

Listed:
  • Davide Raggi

Abstract

In this paper we propose an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate stochastic volatility models with jumps and affine structure. Our idea relies on the use of adaptive methods that aim at reducing the asymptotic variance of the estimates. We focus on the Delayed Rejection algorithm in order to find accurate proposals and to efficiently simulate the volatility path. Furthermore, Bayesian model selection is addressed through the use of reduced runs of the MCMC together with an auxiliary particle filter necessary to evaluate the likelihood function. An empirical application based on the study of the Dow Jones Composite 65 and of the FTSE 100 financial indexes is presented to study some empirical properties of the algorithm implemented. Copyright 2005 Royal Economic Society

Suggested Citation

  • Davide Raggi, 2005. "Adaptive MCMC methods for inference on affine stochastic volatility models with jumps," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 235-250, July.
    • Handle: RePEc:ect:emjrnl:v:8:y:2005:i:2:p:235-250
    as

    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Davide Raggi & Silvano Bordignon, 2011. "Volatility, Jumps, and Predictability of Returns: A Sequential Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 669-695.
    2. Mylène Bédard & Randal Douc & Eric Moulines, 2014. "Scaling Analysis of Delayed Rejection MCMC Methods," Methodology and Computing in Applied Probability, Springer, vol. 16(4), pages 811-838, December.
    3. Ping Chen & Jinde Wang, 2010. "Application in stochastic volatility models of nonlinear regression with stochastic design," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(2), pages 142-156, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    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:ect:emjrnl:v:8:y:2005:i:2:p:235-250. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/resssea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.