We examine the relative risk performance of the Dow Jones Islamic Index (DJIS) and find that the index outperforms the Dow Jones (DJIM) WORLD Index in terms of risk. Using the most recent Value-at-Risk (VaR) methodologies (RiskMetrics, Student-t APARCH, and skewed Student-t APARCH) on the 1996–2005 period, and assuming one-day holding period for both indices with a moving window of 500 day data, we show that the value of VaR is greater for DJIM WORLD than for DJIS Islamic. We interpret the results mainly to the profit-and-loss sharing principle of Islamic finance where banks share the profits and bear losses (Mudarabah) or share both profits and losses (Musharaka) with the firm.

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PurposeThe 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/approachThe 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.FindingsThe 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/implicationsThe 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/valueThe 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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Purpose -In this paper, the aim is to investigate the tail behavior of daily stock returns for three emerging stock in the Gulf region (Bahrain, Oman, and Saudi Arabia) over the period 1998-2005. In addition, the aim is also to test whether the distributions are similar across these markets. Design/methodology/approach -Following McNeil and Frey, Wanger and Marsh, and Bystrom, extreme value theory (EVT) methods are utilized to examine the asymptotic distribution of the tail for daily returns in the Gulf region. As a first step and to obtain independent and identically distributed residuals series, the returns are prefiltered with an ordinary time-series model, taking into account the observed Gulf return dynamics. Then, the "Peaks-Over-Threshold" (POT) model is applied to estimate the tails of the innovational distribution. Findings -Not only is the heavy tail found to be a facial appearance in these markets, but also POT method of modelling extreme tail quantiles is more accurate than conventional methodologies (historical simulation and normal distribution models) in estimating the tail behavior of the Gulf markets returns. Across all return series, it is found that left and right tails behave very different across countries.Research limitations/implications -The results show that risk models that are able to exploit tail behavior could lead to more accurate risk estimates. Thus, participants in the Gulf equity markets can rely on EVT-based risk model when assessing their risks. Originality/value -The paper extends previous studies in two aspects. First, it extends the classical unconditional extreme value approach by first filtering the data by using AR-FIAPARCH model to capture some of the dependencies in the stock returns, and thereafter applying ordinary extreme value techniques. Second, it provides a broad analysis of return dynamics of the Gulf markets.