IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v18y2020i3p556-584..html
   My bibliography  Save this article

Using the Extremal Index for Value-at-Risk Backtesting

Author

Listed:
  • Axel Bücher
  • Peter N Posch
  • Philipp Schmidtke

Abstract

We introduce a set of new Value-at-Risk independence backtests by establishing a connection between the independence property of Value-at-Risk forecasts and the extremal index, a general measure of extremal clustering of stationary sequences. For this purpose, we introduce a sequence of relative excess returns whose extremal index is to be estimated. We compare our backtest to both popular and recent competitors using Monte Carlo simulations and find considerable power in many scenarios. In an applied section, we perform realistic out-of-sample forecasts with common forecasting models and discuss advantages and pitfalls of our approach.

Suggested Citation

  • Axel Bücher & Peter N Posch & Philipp Schmidtke, 2020. "Using the Extremal Index for Value-at-Risk Backtesting," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 556-584.
    • Handle: RePEc:oup:jfinec:v:18:y:2020:i:3:p:556-584.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbaa011
    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. Anna Kiriliouk & Chen Zhou, 2024. "Tail Risk Analysis for Financial Time Series," Papers 2409.18643, arXiv.org.

    More about this item

    Keywords

    VaR backtesting; extremal index; independence; risk measures;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    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:18:y:2020:i:3:p:556-584.. 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.