IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2207.04887.html
   My bibliography  Save this paper

A note on VIX for postprocessing quantitative strategies

Author

Listed:
  • Jun Lu
  • Minhui Wu

Abstract

In this note, we introduce how to use Volatility Index (VIX) for postprocessing quantitative strategies so as to increase the Sharpe ratio and reduce trading risks. The signal from this procedure is an indicator of trading or not on a daily basis. Finally, we analyze this procedure on SH510300 and SH510050 assets. The strategies are evaluated by measurements of Sharpe ratio, max drawdown, and Calmar ratio. However, there is always a risk of loss in trading. The results from the tests are just examples of how the method works; no claim is made on the suggestion of real market positions.

Suggested Citation

  • Jun Lu & Minhui Wu, 2022. "A note on VIX for postprocessing quantitative strategies," Papers 2207.04887, arXiv.org.
    • Handle: RePEc:arx:papers:2207.04887
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2207.04887
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jun Lu, 2022. "Exploring Classic Quantitative Strategies," Papers 2202.11309, arXiv.org.
    2. Jun Lu & Shao Yi, 2022. "Reducing overestimating and underestimating volatility via the augmented blending-ARCH model," Papers 2203.12456, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jun Lu & Shao Yi, 2022. "Reducing Overestimating and Underestimating Volatility via the Augmented Blending-ARCH Model," Applied Economics and Finance, Redfame publishing, vol. 9(2), pages 48-59, May.
    2. Jun Lu & Danny Ding, 2022. "A Hybrid Approach on Conditional GAN for Portfolio Analysis," Papers 2208.07159, arXiv.org.
    3. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Papers 2207.05701, arXiv.org.
    4. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Applied Economics and Finance, Redfame publishing, vol. 9(3), pages 55-68, August.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2207.04887. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.