Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Extreme Quantile Estimation for Dependent Data, with Applications to Finance

Holger Drees
Bernoulli
Vol. 9, No. 4 (Aug., 2003), pp. 617-657
Stable URL: http://www.jstor.org/stable/3318788
Page Count: 41
  • Read Online (Free)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Extreme Quantile Estimation for Dependent Data, with Applications to Finance
Preview not available

Abstract

The asymptotic normality of a class of estimators for extreme quantiles is established under mild structural conditions on the observed stationary β-mixing time series. Consistent estimators of the asymptotic variance are introduced, which render possible the construction of asymptotic confidence intervals for the extreme quantiles. Moreover, it is shown that many well-known time series models satisfy our conditions. The theory is then applied to a time series of stock index returns. Finally, the finite-sample behaviour of the proposed confidence intervals is examined in a simulation study. It turns out that for most time series models under consideration the actual coverage probability is pretty close to the nominal level if the sample fraction used for estimation is chosen appropriately.

Page Thumbnails

  • Thumbnail: Page 
[617]
    [617]
  • Thumbnail: Page 
618
    618
  • Thumbnail: Page 
619
    619
  • Thumbnail: Page 
620
    620
  • Thumbnail: Page 
621
    621
  • Thumbnail: Page 
622
    622
  • Thumbnail: Page 
623
    623
  • Thumbnail: Page 
624
    624
  • Thumbnail: Page 
625
    625
  • Thumbnail: Page 
626
    626
  • Thumbnail: Page 
627
    627
  • Thumbnail: Page 
628
    628
  • Thumbnail: Page 
629
    629
  • Thumbnail: Page 
630
    630
  • Thumbnail: Page 
631
    631
  • Thumbnail: Page 
632
    632
  • Thumbnail: Page 
633
    633
  • Thumbnail: Page 
634
    634
  • Thumbnail: Page 
635
    635
  • Thumbnail: Page 
636
    636
  • Thumbnail: Page 
637
    637
  • Thumbnail: Page 
638
    638
  • Thumbnail: Page 
639
    639
  • Thumbnail: Page 
640
    640
  • Thumbnail: Page 
641
    641
  • Thumbnail: Page 
642
    642
  • Thumbnail: Page 
643
    643
  • Thumbnail: Page 
644
    644
  • Thumbnail: Page 
645
    645
  • Thumbnail: Page 
646
    646
  • Thumbnail: Page 
647
    647
  • Thumbnail: Page 
648
    648
  • Thumbnail: Page 
649
    649
  • Thumbnail: Page 
650
    650
  • Thumbnail: Page 
651
    651
  • Thumbnail: Page 
652
    652
  • Thumbnail: Page 
653
    653
  • Thumbnail: Page 
654
    654
  • Thumbnail: Page 
655
    655
  • Thumbnail: Page 
656
    656
  • Thumbnail: Page 
657
    657