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 ).

Download

Full text not available from this repository.

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
ISSNs: 0927-5398 (print)
Keywords: outliers, outward testing, masking
Subjects: H Social Sciences > HG Finance
Divisions: University Structure - Pre August 2011 > School of Social Sciences > Economics
ePrint ID: 153073
Date Deposited: 18 May 2010 11:33
Last Modified: 27 Mar 2014 19:11
URI: http://eprints.soton.ac.uk/id/eprint/153073

Actions (login required)

View Item View Item