Identifying multiple outliers in heavy-tailed distributions with an application to market crashes


Schluter, Christian and Trede, M. (2007) Identifying multiple outliers in heavy-tailed distributions with an application to market crashes Journal of Empirical Finance, 15, (4), pp. 700-713. (doi:10.1016/j.jempfin.2007.10.003).

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Description/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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.jempfin.2007.10.003
ISSNs: 0927-5398 (print)
Keywords: outliers, outward testing, masking
Subjects:
ePrint ID: 153073
Date :
Date Event
12 November 2007e-pub ahead of print
September 2008Published
Date Deposited: 18 May 2010 11:33
Last Modified: 18 Apr 2017 04:16
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/153073

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