The Role of the Skewed Distributions in the Framework of Extreme Value Theory (EVT)

Sonia Benito Muela, Carmen López-Martín and Ángeles Navarro Mª

International Business Research, 2017, vol. 10, issue 11, 88-102

Abstract: In this paper, we analyze the role of the heavy tail and skewed distribution in market risk estimation (Value at Risk (VaR)). In particular, we are interested in knowing if in the framework of the conditional extreme value theory, the estimation of the volatility model below heavy tail and skewed distribution contributes to improve the VaR estimation respect to these obtained from a symmetric distribution. The study has been carried out for six individual assets belonging to the digital sector: ADP, Amazon, Cerner, Apple, Microsoft and Telefonica. The analysis period runs from January 1st, 2008 to the end of December 2013. Although the evidence found is a little bit weak, the results obtained seem to indicate that the heavy tail and skewed distribution outperforms the symmetric distribution both in terms of accuracy VaR estimations as in terms of firm’s loss function. Furthermore, the market risk capital requirements fixed on the base of the VaR estimations are also lowest below a skewed distribution.

Keywords: extreme value theory; loss functions; skewness distributions; value at risk (search for similar items in EconPapers)
JEL-codes: C15 C22 C52 G17 G32 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ibn:ibrjnl:v:10:y:2017:i:11:p:88-102

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