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

Record type: Article

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.

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Citation

Schluter, Christian and Trede, M. (2008) 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).

More information

e-pub ahead of print date: 12 November 2007
Published date: September 2008
Keywords: outliers, outward testing, masking
Organisations: Economics

Identifiers

Local EPrints ID: 153073
URI: http://eprints.soton.ac.uk/id/eprint/153073
ISSN: 0927-5398
PURE UUID: 534a41eb-088c-41f4-ba5a-ef89213681a2

Catalogue record

Date deposited: 18 May 2010 11:33
Last modified: 18 Jul 2017 12:52

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