IDEAS home Printed from https://ideas.repec.org/p/sbs/wpsefe/2007fe02.html
   My bibliography  Save this paper

Feasible inference for realised variance in the presence of jumps

Author

Listed:
  • Almut Elisabeth Dorothea Veraart

Abstract

Here we assume that the logarithmic asset price is given by a semimartingle. Jacod (2006) has derived an infeasible central limit theorem for the realized variance in such a general framework. However, here we focus on constructing a feasible limit theorem. We propose a new estimator for the asymptotic variance of the realized variance. This new estimator is based on generalized versions of the realized variance and the realized bipower variation. We prove the consistency of this estimator and can derive a feasible limit theorem for the realized variance.

Suggested Citation

  • Almut Elisabeth Dorothea Veraart, 2007. "Feasible inference for realised variance in the presence of jumps," OFRC Working Papers Series 2007fe02, Oxford Financial Research Centre.
  • Handle: RePEc:sbs:wpsefe:2007fe02
    as

    Download full text from publisher

    File URL: http://www.finance.ox.ac.uk/file_links/finecon_papers/2007fe02.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Ilze Kalnina & Oliver Linton, 2007. "Inference about Realized Volatility using Infill Subsampling," STICERD - Econometrics Paper Series 523, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Bipower variation; feasible inference; realized variance; semimartingale; stochastic volatility;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:sbs:wpsefe:2007fe02. 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: Maxine Collett (email available below). General contact details of provider: https://edirc.repec.org/data/frcoxuk.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.