IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v26y2014i2p373-384.html
   My bibliography  Save this article

Volatility estimation from short time series of stock prices

Author

Listed:
  • Nikolai Dokuchaev

Abstract

We consider estimation of the historical volatility of stock prices. It is assumed that the stock prices are represented as time series formed as samples of the solution of a stochastic differential equation with random and time-varying parameters; these parameters are not observable directly and have unknown evolution law. The price samples are available with limited frequency only. In this setting, the estimation has to be based on short time series, and the estimation error can be significant. We suggest some supplements to the existing nonparametric methods of volatility estimation. Two modifications of the standard summation formula for the volatility are derived. In addition, a linear transformation eliminating the appreciation rate and preserving the volatility is suggested.

Suggested Citation

  • Nikolai Dokuchaev, 2014. "Volatility estimation from short time series of stock prices," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(2), pages 373-384, June.
    • Handle: RePEc:taf:gnstxx:v:26:y:2014:i:2:p:373-384
    DOI: 10.1080/10485252.2013.844805
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2013.844805
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2013.844805?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. Ko, Hyungjin & Son, Bumho & Lee, Jaewook, 2024. "Portfolio insurance strategy in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 67(PA).
    2. Chuong Luong & Nikolai Dokuchaev, 2018. "Forecasting of Realised Volatility with the Random Forests Algorithm," JRFM, MDPI, vol. 11(4), pages 1-15, October.
    3. Hong Ben Yee & Nikolai Dokuchaev, 2015. "Construction Of Models For Bounded Price Processes: The Case Of The Hkd Exchange Rate," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-23, December.
    4. Nikolai Dokuchaev, 2017. "A pathwise inference method for the parameters of diffusion terms," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 731-743, October.

    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:gnstxx:v:26:y:2014:i:2:p:373-384. 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/GNST20 .

    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.