Parametric VaR with goodness-of-fit tests based on EDF statistics for extreme returns
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
27/05/2014
27/05/2014
01/11/2013
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Resumo |
Parametric VaR (Value-at-Risk) is widely used due to its simplicity and easy calculation. However, the normality assumption, often used in the estimation of the parametric VaR, does not provide satisfactory estimates for risk exposure. Therefore, this study suggests a method for computing the parametric VaR based on goodness-of-fit tests using the empirical distribution function (EDF) for extreme returns, and compares the feasibility of this method for the banking sector in an emerging market and in a developed one. The paper also discusses possible theoretical contributions in related fields like enterprise risk management (ERM). © 2013 Elsevier Ltd. |
Formato |
1648-1658 |
Identificador |
http://dx.doi.org/10.1016/j.mcm.2013.07.002 Mathematical and Computer Modelling, v. 58, n. 9-10, p. 1648-1658, 2013. 0895-7177 http://hdl.handle.net/11449/76900 10.1016/j.mcm.2013.07.002 WOS:000325306700007 2-s2.0-84883557862 |
Idioma(s) |
eng |
Relação |
Mathematical and Computer Modelling |
Direitos |
closedAccess |
Palavras-Chave | #Anderson-Darling #Goodness-of-fit tests #Kolmogorov-Smirnov #Parametric Value-at-Risk #Tails #Goodness-of-fit test #Value at Risk #Risk management #Value engineering #Parameter estimation |
Tipo |
info:eu-repo/semantics/article |