IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

Estimation of the characteristics of a Lévy process observed at arbitrary frequency

Author Info

Listed author(s):
  • Johanna Kappus
  • Markus Reiß
Registered author(s):

    Abstract

    No abstract is available for this item.

    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.1467-9574.2010.00461.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.

    Article provided by Netherlands Society for Statistics and Operations Research in its journal Statistica Neerlandica.

    Volume (Year): 64 (2010)
    Issue (Month): s1 ()
    Pages: 314-328

    as
    in new window

    Handle: RePEc:bla:stanee:v:64:y:2010:i:s1:p:314-328
    Contact details of provider: Web page: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402

    Order Information: Web: http://www.blackwellpublishing.com/subs.asp?ref=0039-0402

    Keywords:

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

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

    as
    in new window


    Cited by:

    1. Kappus, Johanna, 2014. "Adaptive nonparametric estimation for Lévy processes observed at low frequency," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 730-758.
    2. Reiß, Markus, 2013. "Testing the characteristics of a Lévy process," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2808-2828.
    3. Mathias Trabs, 2011. "Calibration of selfdecomposable Lévy models," SFB 649 Discussion Papers SFB649DP2011-073, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Trabs, Mathias, 2015. "Quantile estimation for Lévy measures," Stochastic Processes and their Applications, Elsevier, vol. 125(9), pages 3484-3521.
    5. Zhang, Zhimin & Yang, Hailiang, 2014. "Nonparametric estimation for the ruin probability in a Lévy risk model under low-frequency observation," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 168-177.

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

    Access and download statistics

    When requesting a correction, please mention this item's handle: RePEc:bla:stanee:v:64:y:2010:i:s1:p:314-328. 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: (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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.