IDEAS home Printed from https://ideas.repec.org/a/spr/alstar/v90y2006i2p273-297.html
   My bibliography  Save this article

On the appropriateness of inappropriate VaR models

Author

Listed:
  • Wolfgang Härdle
  • Zdeněk Hlávka
  • Gerhard Stahl

Abstract

No abstract is available for this item.

Suggested Citation

  • Wolfgang Härdle & Zdeněk Hlávka & Gerhard Stahl, 2006. "On the appropriateness of inappropriate VaR models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(2), pages 273-297, June.
    • Handle: RePEc:spr:alstar:v:90:y:2006:i:2:p:273-297
    DOI: 10.1007/s10182-006-0234-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10182-006-0234-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10182-006-0234-0?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.

    References listed on IDEAS

    as
    1. Murphy, Allan H. & Winkler, Robert L., 1992. "Diagnostic verification of probability forecasts," International Journal of Forecasting, Elsevier, vol. 7(4), pages 435-455, March.
    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. Unn Lindholm & Marcus Mossfeldt & Pär Stockhammar, 2020. "Forecasting inflation in Sweden," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(1), pages 39-68, April.
    2. Kajal Lahiri & J. George Wang, 2007. "The value of probability forecasts as predictors of cyclical downturns," Applied Economics Letters, Taylor & Francis Journals, vol. 14(1), pages 11-14.
    3. Hanea, A.M. & McBride, M.F. & Burgman, M.A. & Wintle, B.C. & Fidler, F. & Flander, L. & Twardy, C.R. & Manning, B. & Mascaro, S., 2017. "I nvestigate D iscuss E stimate A ggregate for structured expert judgement," International Journal of Forecasting, Elsevier, vol. 33(1), pages 267-279.
    4. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
    5. Shinitzky, Hilla & Shemesh, Yhonatan & Leiser, David & Gilead, Michael, 2024. "Improving geopolitical forecasts with 100 brains and one computer," International Journal of Forecasting, Elsevier, vol. 40(3), pages 958-970.
    6. Martin Kukuk & Michael Rönnberg, 2013. "Corporate credit default models: a mixed logit approach," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 467-483, April.
    7. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    8. David J. Johnstone, 2007. "The Parimutuel Kelly Probability Scoring Rule," Decision Analysis, INFORMS, vol. 4(2), pages 66-75, June.
    9. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Can Macroeconomists Forecast Risk? Event-Based Evidence from the Euro-Area SPF," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 1-46, December.
    10. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    11. Nesvold, Erik & Bratvold, Reidar B., 2022. "Debiasing probabilistic oil production forecasts," Energy, Elsevier, vol. 258(C).
    12. Yates, J. Frank & Price, Paul C. & Lee, Ju-Whei & Ramirez, James, 1996. "Good probabilistic forecasters: The 'consumer's' perspective," International Journal of Forecasting, Elsevier, vol. 12(1), pages 41-56, March.
    13. Dimitriadis, Timo & Gneiting, Tilmann & Jordan, Alexander I. & Vogel, Peter, 2024. "Evaluating probabilistic classifiers: The triptych," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1101-1122.
    14. Wilkie-Thomson, Mary E. & Onkal-Atay, Dilek & Pollock, Andrew C., 1997. "Currency forecasting: an investigation of extrapolative judgement," International Journal of Forecasting, Elsevier, vol. 13(4), pages 509-526, December.
    15. JS Armstrong & Robert Fildes, 2004. "Correspondence On the Selection of Error Measures for Comparisons Among Forecasting Methods," General Economics and Teaching 0412002, University Library of Munich, Germany.
    16. R. Winkler & Javier Muñoz & José Cervera & José Bernardo & Gail Blattenberger & Joseph Kadane & Dennis Lindley & Allan Murphy & Robert Oliver & David Ríos-Insua, 1996. "Scoring rules and the evaluation of probabilities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(1), pages 1-60, June.
    17. Clemen, Robert T. & Murphy, Allan H. & Winkler, Robert L., 1995. "Screening probability forecasts: contrasts between choosing and combining," International Journal of Forecasting, Elsevier, vol. 11(1), pages 133-145, March.
    18. A. Dawid & M. DeGroot & J. Mortera & R. Cooke & S. French & C. Genest & M. Schervish & D. Lindley & K. McConway & R. Winkler, 1995. "Coherent combination of experts' opinions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 4(2), pages 263-313, December.
    19. Abramson, Bruce & Brown, John & Edwards, Ward & Murphy, Allan & Winkler, Robert L., 1996. "Hailfinder: A Bayesian system for forecasting severe weather," International Journal of Forecasting, Elsevier, vol. 12(1), pages 57-71, March.
    20. D. J. Johnstone & S. Jones & V. R. R. Jose & M. Peat, 2013. "Measures of the economic value of probabilities of bankruptcy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 635-653, June.

    More about this item

    Keywords

    Value-at-Risk; market index model; principal components; random effects model; probability forecast; JEL C51; C52; G20;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    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:alstar:v:90:y:2006:i:2:p:273-297. 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: 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.