IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v2y2005i2p87-106.html
   My bibliography  Save this article

A multivariate FGD technique to improve VaR computation in equity markets

Author

Listed:
  • Francesco Audrino
  • Giovanni Barone-Adesi

Abstract

It is difficult to compute Value-at-Risk (VaR) using multivariate models able to take into account the dependence structure between large numbers of assets and being still computationally feasible. A possible procedure is based on functional gradient descent (FGD) estimation for the volatility matrix in connection with asset historical simulation. Backtest analysis on simulated and real data provides strong empirical evidence of the better predictive ability of the proposed procedure over classical filtered historical simulation, with a resulting significant improvement in the measurement of risk. Copyright Springer-Verlag Berlin/Heidelberg 2005

Suggested Citation

  • Francesco Audrino & Giovanni Barone-Adesi, 2005. "A multivariate FGD technique to improve VaR computation in equity markets," Computational Management Science, Springer, vol. 2(2), pages 87-106, March.
    • Handle: RePEc:spr:comgts:v:2:y:2005:i:2:p:87-106
    DOI: 10.1007/s10287-004-0028-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10287-004-0028-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10287-004-0028-3?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. Fabio Trojani & Francesco Audrino, 2005. "Accurate Yield Curve Scenarios Generation using Functional Gradient Descent," Computing in Economics and Finance 2005 14, Society for Computational Economics.
    2. Pritsker, Matthew, 2006. "The hidden dangers of historical simulation," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 561-582, February.
    3. Audrino, Francesco & Barone-Adesi, Giovanni, 2005. "Functional gradient descent for financial time series with an application to the measurement of market risk," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 959-977, April.
    4. Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 591-623, Fall.
    5. Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.

    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:spr:comgts:v:2:y:2005:i:2:p:87-106. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.