IDEAS home Printed from https://ideas.repec.org/p/hst/ghsdps/gd11-195.html
   My bibliography  Save this paper

Quantile Forecasts of Financial Returns Using Realized GARCH Models

Author

Listed:
  • Toshiaki Watanabe

Abstract

This article applies the realized GARCH model, which incorporates the GARCH model with realized volatility (RV), to quantile forecasts of financial returns such as Value-at-Risk and expected shortfall. This model has certain advantages in the application to quantile forecasts because it can adjust the bias of RV casued by microstructure noise and non-trading hours and enables us to estimate the parameters in the return distribution jointly with the other parameters. Student's t- and skewed strudent's t-distributions as well as normal distribution are used for the return distribution. The EGARCH model is used for comparison. Main results for the S&P 500 stock index are: (1) the realized GARCH model with the skewed student's t-distribution performs better than that with the normal and student's t-distributions and the EGARCH model using the daily returns only, and (2) the performance does not improve if the realized kernel, which takes account of microstructure noise, is used instead of the plain realized volatility, implying that the realized GARCH model can adjust the bias of RV caused by microstructure noise.

Suggested Citation

  • Toshiaki Watanabe, 2011. "Quantile Forecasts of Financial Returns Using Realized GARCH Models," Global COE Hi-Stat Discussion Paper Series gd11-195, Institute of Economic Research, Hitotsubashi University.
  • Handle: RePEc:hst:ghsdps:gd11-195
    as

    Download full text from publisher

    File URL: http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd11-195.pdf
    Download Restriction: no

    More about this item

    Keywords

    Expected shortfall; GARCH; Realized volatility; Skewed student's t-distribution; Value-at-Risk;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hst:ghsdps:gd11-195. 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: (Tatsuji Makino). General contact details of provider: http://edirc.repec.org/data/iehitjp.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.