Identifying multiple outliers in heavy-tailed distributions with an application to market crashes
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), 700-713. (doi:10.1016/j.jempfin.2007.10.003 ).
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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.
|Keywords:||outliers, outward testing, masking|
|Subjects:||H Social Sciences > HG Finance|
|Divisions:||University Structure - Pre August 2011 > School of Social Sciences > Economics
|Date Deposited:||18 May 2010 11:33|
|Last Modified:||25 Apr 2013 22:45|
|Contributors:||Schluter, Christian (Author)
Trede, M. (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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