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Fitting heavy Tail distributions with mixture models
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Autores
Orientador(es)
Resumo(s)
The normal probability distribution as assumption for financial returns have been recognized as inappropriate, and a source of inaccurate estimation of Value at Risk. Empirical evidence also have been shown that financial returns shows a more accentuated leptokurtic distribution when compared with a Normal distribution and
also skewed. This is usually a cause of underestimated values of VaR, specially when the quantiles are very low. Therefore it is necessary to focus on the tail of the distribution and identify models to fit that behavior. We will highlight the differences between the quality of fitting in the tails of the distribution and the fitting for all the
distribution.
This work compares and interprets the results obtained by applying mixture models as a method to estimate the behavior on the extremes for heavy tail data distributions. This results will be then used to describe an analytical solution of VaR under mixture models.
Descrição
Palavras-chave
Mixture models Extreme values VaR Risk analyses
Contexto Educativo
Citação
Editora
ISAST. International Society for the Advancement of Science and Technology