IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v107y2012i499p1217-1229.html
   My bibliography  Save this article

Mode Identification of Volatility in Time-Varying Autoregression

Author

Listed:
  • Gabriel Chandler
  • Wolfgang Polonik

Abstract

In many applications, time series exhibit nonstationary behavior that might reasonably be modeled as a time-varying autoregressive (AR) process. In the context of such a model, we discuss the problem of testing for modality of the variance function. We propose a test of modality that is local and, when used iteratively, can be used to identify the total number of modes in a given series. This problem is closely related to peak detection and identification, which has applications in many fields. We propose a test that, under appropriate assumptions, is asymptotically distribution free under the null hypothesis, even though nonparametric estimation of the AR parameter functions is involved. Simulation studies and applications to real datasets illustrate the behavior of the test.

Suggested Citation

  • Gabriel Chandler & Wolfgang Polonik, 2012. "Mode Identification of Volatility in Time-Varying Autoregression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1217-1229, September.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:1217-1229
    DOI: 10.1080/01621459.2012.703877
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2012.703877
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2012.703877?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Valentin Patilea & Hamdi Raïssi, 2014. "Testing Second-Order Dynamics for Autoregressive Processes in Presence of Time-Varying Variance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1099-1111, September.
    2. Gabe Chandler & Wolfgang Polonik, 2017. "Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 72-98, January.

    More about this item

    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:taf:jnlasa:v:107:y:2012:i:499:p:1217-1229. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.