Identifying multiple outliers in heavy-tailed distributions with an application to market crashes
Zusammenfassung
Heavy-tailed distributions, such as the distribution of stock returns, are prone to generate large values. This renders difficult the detection of outliers. We propose a new outward testing procedure to identify multiple outliers in these distributions. A major virtue of the test is its simplicity. The performance of the test is investigated in several simulation studies. As a substantive empirical contribution we apply the test to Dow Jones Industrial Average return data and find that the Black Monday market crash was not a structurally unusual event. (C) 2007 Elsevier B.V. All rights reserved.
Schlüsselwörter
outliers outward testing masking stable random-variables regular variation size distribution parameters exponent behavior
Zitieren als
Schluter, C., & Trede, M. (2008). Identifying multiple outliers in heavy-tailed distributions with an application to market crashes. Journal of Empirical Finance, 15(4), 700–713.Details
Publikationstyp
Forschungsartikel (Zeitschrift)
Begutachtet
Ja
Publikationsstatus
Veröffentlicht
Jahr
2008
Fachzeitschrift
Journal of Empirical Finance
Band
15
Ausgabe
4
Erste Seite
700
Letzte Seite
713
Sprache
Englisch
ISSN
0927-5398
DOI