IDEAS home Printed from https://ideas.repec.org/p/sek/iefpro/14115804.html
   My bibliography  Save this paper

Modelling Intraday Realized Volatility: The Role Of Vix, Oil And Gold

Author

Listed:
  • Avraham Turgeman

    (West University of Timisoara)

  • Claudiu Botoc

    (West University of Timisoara)

  • Marilen Pirtea

    (West University of Timisoara)

  • Octavian Jude

    (West University of Timisoara)

Abstract

The main aim of the paper is to test an autoregressive implied volatility (IV) model that can significantly predict realized volatility (RV) of stock index. Subsequently, we want to test the predictive power of products that are external to the index of interest (S&P), by including certain commodities that are derived from VIX, i.e., crude oil and gold. The results do not reject the memory effect, given the predictive power of several lags for VIX over realized volatility. Furthermore, crude oil volatility is a significant predictor, alternatively in realized volatility and implied volatility. Finally, gold implied volatility (with higher lags) predicts stock returns volatility, which suggests a gap since traders tend to start gaining gold earlier to be on the safe side. Our findings have certain implications for trading and risk estimation.

Suggested Citation

  • Avraham Turgeman & Claudiu Botoc & Marilen Pirtea & Octavian Jude, 0000. "Modelling Intraday Realized Volatility: The Role Of Vix, Oil And Gold," Proceedings of Economics and Finance Conferences 14115804, International Institute of Social and Economic Sciences.
  • Handle: RePEc:sek:iefpro:14115804
    as

    Download full text from publisher

    File URL: https://iises.net/proceedings/international-conference-on-economics-finance-and-business-prague-2023-1/table-of-content/detail?cid=141&iid=021&rid=15804
    File Function: First version, 0000
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Implied volatility; Realized volatility; AR model; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:sek:iefpro:14115804. 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: Klara Cermakova (email available below). General contact details of provider: https://iises.net/ .

    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.