Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/13332
Title: Value-at-risk and extreme value distributions for financial returns
Authors: Tolikas, K
Keywords: Extreme Value Theory;Value-at-Risk;L-moments;Probability Weighted Moments;Anderson-Darling goodness of fit test;Generalised Extreme Value distribution;Generalised Logistic distribution
Issue Date: 2008
Publisher: Incisive Media
Citation: The Journal of Risk,10 (3): pp. 31 - 77,(2008)
Abstract: The ability of the Generalised Extreme Value (GEV) and Generalised Logistic (GL) distributions to fit extreme financial returns in the stock, commodities and bond markets is assessed. The empirical results indicate that the too much celebrated GEV is not the most appropriate model for the data since the fatter tailed GL is found to provide better descriptions of the extreme returns. Extreme Value Theory (EVT) based VaR estimates are then derived and compared to those generated by traditional methods. The results show that when the focus is on the really ruinous events which are located deep into the tails of the returns distribution, the EVT methods used in this study can be particularly useful since they produce VaR estimates that outperform those derived by the traditional methods at high confidence levels. However, these estimates were found to be considerably higher than those derived by traditional VaR models; consequently leading to higher capital reserves for financial institutions.
URI: http://bura.brunel.ac.uk/handle/2438/13332
DOI: http://dx.doi.org/10.21314/JOR.2008.174
ISSN: 1465-1211
Appears in Collections:Dept of Economics and Finance Research Papers

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