IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v20y2022i4p655-680..html
   My bibliography  Save this article

Forecasting VIX Using Filtered Historical Simulation
[A GARCH Option Pricing Model with Filtered Historical Simulation]

Author

Listed:
  • Yushuang Jiang
  • Emese Lazar

Abstract

We propose a new VIX forecast method using Generalized Autoregressive Conditional Heteroscedasticity models based on the filtered historical simulation put forward in Barone-Adesi, Engle, and Mancini (2008). The flexible change of measure accommodates for non-normalities such as negative skewness and positive excess kurtosis. We present an application for four well-established volatility indices (VIX9D, VIX, VIX3M, and VIX6M). Our results show that our proposed estimation method outperforms the Normal-VIX model of Hao and Zhang (2013) both in-sample and out-of-sample. Furthermore, the use of volatility indices reduces the computational burden significantly compared to the options-based pricing method.

Suggested Citation

  • Yushuang Jiang & Emese Lazar, 2022. "Forecasting VIX Using Filtered Historical Simulation [A GARCH Option Pricing Model with Filtered Historical Simulation]," Journal of Financial Econometrics, Oxford University Press, vol. 20(4), pages 655-680.
    • Handle: RePEc:oup:jfinec:v:20:y:2022:i:4:p:655-680.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbaa041
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Wu, Xinyu & Zhao, An & Liu, Li, 2023. "Forecasting VIX using two-component realized EGARCH model," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).

    More about this item

    Keywords

    GARCH; historical filtered simulation; CBOE volatility index;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • 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:oup:jfinec:v:20:y:2022:i:4:p:655-680.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.html .

    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.