IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v41y2020i1p154-162.html
   My bibliography  Save this article

Inference for asymmetric exponentially weighted moving average models

Author

Listed:
  • Dong Li
  • Ke Zhu

Abstract

The exponentially weighted moving average (EWMA) model in ‘Risk‐Metrics’ has been a benchmark for controlling and forecasting risks in financial operations. However, it is incapable of capturing the asymmetric volatility effect and the heavy‐tailed innovation, which are two important stylized features of financial returns. We propose a new asymmetric EWMA model driven by the Student's t‐distributed innovations to take these two stylized features into account and study its maximum likelihood estimation and model diagnostic checking. The finite‐sample performance of the estimation and diagnostic test statistic is examined by the simulated data.

Suggested Citation

  • Dong Li & Ke Zhu, 2020. "Inference for asymmetric exponentially weighted moving average models," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(1), pages 154-162, January.
  • Handle: RePEc:bla:jtsera:v:41:y:2020:i:1:p:154-162
    DOI: 10.1111/jtsa.12464
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jtsa.12464
    Download Restriction: no

    File URL: https://libkey.io/10.1111/jtsa.12464?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
    ---><---

    Citations

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


    Cited by:

    1. Li, Dong & Tao, Yuxin & Yang, Yaxing & Zhang, Rongmao, 2023. "Maximum likelihood estimation for α-stable double autoregressive models," Journal of Econometrics, Elsevier, vol. 236(1).

    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:bla:jtsera:v:41:y:2020:i:1:p:154-162. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.