Advanced Search
MyIDEAS: Login

Numerical integration-based Gaussian mixture filters for maximum likelihood estimation of asymmetric stochastic volatility models

Contents:

Author Info

  • Hiroyuki Kawakatsu
Registered author(s):

    Abstract

    I consider Gaussian filters based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. I show that for this class of models, the prediction step of the Gaussian filter can be evaluated analytically without linearizing the state--space model. Monte Carlo simulations show that the mixture Gaussian filter performs remarkably well in terms of both accuracy and computation time compared to the quasi-maximum likelihood and importance sampler filters. The result that the prediction step of the Gaussian filter can be evaluated analytically is shown to apply more generally to a number of commonly used specifications of the stochastic volatility model. Copyright Royal Economic Society 2007

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2007.00211.x
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Royal Economic Society in its journal Econometrics Journal.

    Volume (Year): 10 (2007)
    Issue (Month): 2 (07)
    Pages: 342-358

    as in new window
    Handle: RePEc:ect:emjrnl:v:10:y:2007:i:2:p:342-358

    Contact details of provider:
    Web page: http://www.res.org.uk/
    More information through EDIRC

    Order Information:
    Web: http://www.ectj.org

    Related research

    Keywords:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

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

    Cited by:
    1. Tore Selland Kleppe & Hans J. Skaug & Jun Yu, 2009. "Simulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models," Working Papers CoFie-09-2009, Sim Kee Boon Institute for Financial Economics.
    2. Isao Ishida & Michael McAleer & Kosuke Oya, 2011. "Estimating the Leverage Parameter of Continuous-time Stochastic Volatility Models Using High Frequency S&P 500 and VIX," KIER Working Papers 759, Kyoto University, Institute of Economic Research.
    3. Almut Veraart, 2011. "How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?," AStA Advances in Statistical Analysis, Springer, vol. 95(3), pages 253-291, September.
    4. Tore Selland Kleppe & Jun Yu & Hans J. skaug, 2011. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers 10-2011, Singapore Management University, School of Economics.
    5. Cai, Lili & Swanson, Norman R., 2011. "In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982-2008," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 743-764, September.
    6. Carles Bretó & Helena Veiga, 2011. "Forecasting volatility: does continuous time do better than discrete time?," Statistics and Econometrics Working Papers ws112518, Universidad Carlos III, Departamento de Estadística y Econometría.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:ect:emjrnl:v:10:y:2007:i:2:p:342-358

    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).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.