Identifying multiple outliers in heavy-tailed distributions with an application to market crashes
Abstract
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.
Keywords
outliers outward testing masking stable random-variables regular variation size distribution parameters exponent behavior
Cite as
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
Publication type
Research article (journal)
Peer reviewed
Yes
Publication status
Published
Year
2008
Journal
Journal of Empirical Finance
Volume
15
Issue
4
Start page
700
End page
713
Language
English
ISSN
0927-5398
DOI