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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.

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.

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.

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.

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.

This study aims to examine the asymmetric effects of average temperature (TP) and rainfall (RF) on the Moroccan food security, measured by the food production index (FPI), using annual data from 1961 to 2020.

The study uses the Climate Change and Food Security Framework (CCFS) developed by the Food and Agriculture Organization (FAO) and employs the nonlinear auto-regressive distributed lag (NARDL) model and various econometric techniques to show the effects of climate variability in the short and long-term. It also examines if the impacts on Moroccan food security are asymmetric by analyzing the positive and negative partial sums of mean temperature and rainfall.

The study shows that RF has a long-term relationship with FPI, with increased RF leading to increased FPI and decreased RF leading to decreased FPI. FPI responds more strongly and persistently to a positive shock in RF than to an adverse shock. The study also identifies an asymmetric relationship between FPI and RF, with increased TP enhancing food output in the long run and a decrease reducing food production in the long run.

The current study could have some limitations. For instance, there are several other non-climate factors that might potentially impact food security. In particular, CO2 emissions which from the literature is a key variable that represent climate change impact on food security, was not included. The present research has not included those factors mainly because adding more variables to the model reduces the degree of freedom available to estimate the parameters, resulting in inaccurate results.

This paper contributes to the food security literature by utilizing the latest asymmetry methodology to decompose climate changes into their positive and negative trends and examining the contrasting impacts food production.

This study aims to provide a new method for precisely sizing photovoltaic (PV) arrays for standalone, direct pumping PV Water Pumping (PVWP) systems for irrigation purposes.

The method uses historical weather data and considers daily variability in regional temperatures and rainfall, crop evapotranspiration rates and seasonality effects, all within a nonparametric bootstrapping approach to synthetically generate daily rainfall and crop irrigation needs. These needs define the required daily supply of pumped water to achieve a user-specified level of reliability, which provides the input to an intuitive approach for PV array sizing. An economic comparison of the costs for the PVWP versus a comparably powered diesel generator system is provided.

Pumping 22.8646 m³/day of water would meet the pasture crop irrigation needs on a one-acre (4046.78 m²) tract of land in South Florida, with 99.9% reliability. Given the specified assumptions, an 8.4834 m² PV array, having a peak power of 1.1877 (kW), could provide the 1.2347 (kWh/day) of hydraulic energy needed to supply this volume over a total head of 20 meters. The PVWP system is the low-cost option when diesel prices are above $0.90/liter and total installed PV array costs are fixed at $2.00/Watt peak power or total installed PV array costs are below $1.50/Watt peak power and diesel prices are fixed at $0.65/liter.

Because the approach is not dependent on the shapes of the sampling distributions for regional climate factors and can be adapted to consider different types of crops, it is highly portable and applicable for precisely determining array sizes for standalone, direct pumping PVWP systems for irrigating diverse crop types in diverse regions.