IDEAS home Printed from https://ideas.repec.org/p/pdn/dispap/30.html
   My bibliography  Save this paper

A comparison study of realized kernels using different sampling frequencies

Author

Listed:
  • Chen Zhou

    (University of Paderborn)

Abstract

A crucial problem by applying realized kernels (RK) is the selection of the bandwidth. We improve the iterative plug-in (IPI) algorithms for selecting bandwidth of RK under independent (Feng and Zhou, 2015b) and dependent microstructure (MS) noise assumptions (Wang, 2014). The realized estimators calculated by both algorithms are consistent to the daily integrated volatility. The nice practical performance of these algorithms are illustrated by the application to 9 years of data on 10 European firms. Moreover, using these two algorithms we calculate RK based on different sampling frequencies and compare them with several other realized estimators. The comparison of these realized estimators is carried out by assessing their performances in the computation of Value-at-Risk (VaR) based on the Semi-FI-Log-ACD model. The RK estimators based on the tick-by-tick returns calculated by both IPI algorithms mentioned above have good performances and are hence recommended using as the estimators of volatility in practice.

Suggested Citation

  • Chen Zhou, 2018. "A comparison study of realized kernels using different sampling frequencies," Working Papers Dissertations 30, Paderborn University, Faculty of Business Administration and Economics.
  • Handle: RePEc:pdn:dispap:30
    as

    Download full text from publisher

    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/dispap/DP30.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Realized volatility; realized kernels; microstructure noise; bandwidth selection; iterative plug-in; sampling frequency; Value-at-Risk;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:pdn:dispap:30. 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: WP-WiWi-Info (email available below). General contact details of provider: https://edirc.repec.org/data/fwpadde.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.