Purpose – The paper aims to investigate the relative performance of the most popular value-at-risk (VaR) estimates with an emphasis on the extreme value theory (EVT) methodology for seven Middle East and North Africa (MENA) countries. Design/methodology/approach – The paper calculates tails distributions of return series by EVT. This allows computing VaR and comparing the results with Variance-Covariance method, Historical simulation, and ARCH-type process with normal distribution, Student-t distribution and skewed Student-t distribution. The paper assesses the performance of the models, which are used in VaR estimations, based on their empirical failure rates. Findings – The empirical results demonstrate that the return distributions of the MENA markets are characterized by fat tails which implies that VaR measures relies on the normal distribution will underestimate VaR. The results suggest that the extreme value approach, by modeling the tails of the return distributions, are more relevant to measure VaR in most of the MENA. Research limitations/implications – The results show that the use of conventional methodologies such as the normal distribution model to estimate the financial market risk in MENA countries may lead to faulty estimation of risk in the world of volatile markets. Originality/value – The paper tried to fill the gap in the literature and perform an evaluation of the relative performance of the most popular VaR estimates with an emphasis on the EVT methodology in seven MENA emerging stock markets. A comparison of the performance between EVT and other VaR techniques should support the decision whether more or less sophisticated methods are appropriate in order to assess stock market risks in the MENA countries.
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