Selection of Value-at-Risk models for MENA Islamic indices

Abstract

Purpose The purpose of this study is to investigate the performance of Value-at-Risk (VaR) models for nine Middle East and North Africa Islamic indices using RiskMetrics and VaR parametric models. Design/methodology/approach The authors test the performance of several VaR models using Kupiec and Engle and Manganelli tests at 95 and 99 per cent levels for long and short trading positions, respectively, for the period from August 10, 2006 to December 14, 2014. Findings The authors’ findings show that the VaR under Student and skewed Student distribution are preferred at a 99 per cent level VaR. However, at 95 per cent level, the VaR forecasts obtained under normal distribution are more accurate than those generated using models with fat-tailed distributions. These results suggest that VaR is a good tool for measuring market risk. The authors support the use of RiskMetrics during calm periods and the asymmetric models (Generalized Autoregressive Conditional Heteroskedastic and the Asymmetric Power ARCH model) during stressed periods. Practical implications These results will be useful to investors and risk managers operating in Islamic markets, because their success depends on the ability to forecast stock price movements. Therefore, because a few Islamic financial institutions use internal models for their capital calculations, the regulatory committee should enhance market risk disclosure. Originality/value This study contributes to the knowledge in this area by improving our understanding of market risk management for Islamic assets during the stress periods. Then, it highlights important implications regarding financial risk management. Finally, this study fills a gap in the literature, as most empirical studies dealing with evaluating VaR prediction models have focused on quantifying the model risk in the conventional market.

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“…Backtesting-the threezone approach PRR FYGARCH). Similar findings have been revealed for the MENA Islamic indices during the "Arab Spring" (Assaf, 2015;Ben Ayed et al, 2020). Finally, EVT is clearly a suitable approach to rely upon during stressed periods, such as that of the pandemic crisis.…”

“…The second group of studies has focused on parametric approaches such as Riskmetrics (Morgan, 1996) and volatility models (Merton, 1980;Taylor, 1982;Bollerslev, 1986;Baillie et al, 1996). The comparison of the various models reveals the following results: First, Riskmetrics perform well in forecasting VaR during the calm period (Gonz alez-Rivera et al, 2004;McMillan and Kambouroudis, 2009;Degiannakis et al, 2012;Ben Ayed et al, 2020). However, the GARCH extension models outperform all models during crisis periods (Bali and Theodossiou, 2007;Orhan and K€ oksal, 2012;Chau et al, 2014;Zhang et al, 2018).…”

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“…In addition, S-GARCH and GJR-GARCH models underestimated VaR with student-t innovation. Ben Ayed et al [11] explored the performance of Value at Risk models for North Africa and Middle East Islamic indices by using risk metrics and other GARCH models. They suggested using risk metrics in calm periods and both GARCH and APARCH in turbulence periods.…”