IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v13y2015i3p722-755..html
   My bibliography  Save this article

The HESSIAN Method for Models with Leverage-like Effects

Author

Listed:
  • Barnabé Djegnéné
  • William J. McCausland

Abstract

We propose a new method for simulation smoothing in state space models with univariate states and conditional dependence between the observation yt and the contemporaneous innovation of the state equation. Stochastic volatility models with the leverage effect are a leading example. Our method extends the HESSIAN method of McCausland (2012, Journal of Econometrics, 168, 189–206), which required conditional independence between yt and the state innovation. Our generic method is more numerically efficient than the model-specific methods of Omori et al. (2007, J. Fin. Econ., 140, 425–449)—for a stochastic volatility model with Gaussian innovations—and Nakajima and Omori (2009, Comput. Stat. Data Anal., 53, 2335–2353)—for a model with Student's t innovations.

Suggested Citation

  • Barnabé Djegnéné & William J. McCausland, 2015. "The HESSIAN Method for Models with Leverage-like Effects," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 13(3), pages 722-755.
  • Handle: RePEc:oup:jfinec:v:13:y:2015:i:3:p:722-755.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbt027
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    3. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
    4. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.

    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:oup:jfinec:v:13:y:2015:i:3:p:722-755.. 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: (Oxford University Press). General contact details of provider: http://edirc.repec.org/data/sofieea.html .

    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.