IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v21y2015i6p486-506.html
   My bibliography  Save this article

How candlestick features affect the performance of volatility forecasts: evidence from the stock market

Author

Listed:
  • Jung-Bin Su

Abstract

In this study, we used asymmetric GJR-X models to investigate how the return and volatility estimates in the stock market on any given day are affected by the features of the preceding day's candlestick. Empirical results show that, first, for symmetric volatility specification, the upper and lower shadows of yesterday can, respectively, lower and raise the return today, whereas both upper and lower shadows of yesterday can increase today's volatility. Notably, the upper and lower shadows elicited asymmetric responses in the sizes of the volatility and return increments. Conversely, for asymmetric volatility specification, leverage effect may affect the asymmetric response and prevent the upper shadow from influencing the return and volatility. Second, for symmetric volatility specification, the black and white real bodies of yesterday can, respectively, augment and abate today's return and volatility, indicating that the black real body produces a distinct type of leverage effect to influence volatility. Importantly, for asymmetric specification, the effects of the black and white real bodies appear the same as for the symmetric specification, but are less significant. Lastly, the real bodies (or, respectively, asymmetric volatility specification) influenced the accuracy of volatility forecasts more strongly than the upper and lower shadows (or, respectively, symmetric volatility specification).

Suggested Citation

  • Jung-Bin Su, 2015. "How candlestick features affect the performance of volatility forecasts: evidence from the stock market," The European Journal of Finance, Taylor & Francis Journals, vol. 21(6), pages 486-506, April.
  • Handle: RePEc:taf:eurjfi:v:21:y:2015:i:6:p:486-506
    DOI: 10.1080/1351847X.2013.850440
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1351847X.2013.850440
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1351847X.2013.850440?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. Xu, Weijun & Liu, Guifang & Li, Hongyi, 2016. "A novel jump diffusion model based on SGT distribution and its applications," Economic Modelling, Elsevier, vol. 59(C), pages 74-92.

    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:eurjfi:v:21:y:2015:i:6:p:486-506. 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/REJF20 .

    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.