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In this work, we backtest and compare, under the VaR risk measure, the fitting performances of three classes of density distributions (Gaussian, Stable and Pareto) with respect to three different types of emerging markets: Egypt, Qatar and Mexico. We also propose a new technique for the estimation of the Pareto tail index by means of the Threshold Accepting (TAVaR) and the Hybrid Particle Swarm Optimization algorithm (H-PSOVaR). Furthermore, we test the accuracy and robustness of our estimates demonstrating the effectiveness of the proposed approach. • We test the Normal, Stable and Pareto fitting properties over three emerging markets. • We propose two metaheuristic VaR based algorithms, i.e., the TAVaR and the H-PSOVaR. • We run a Monte Carlo simulation to assess the performances of the proposed algorithms. • We test the estimates robustness by measuring their sensitivity to data contamination. [ABSTRACT FROM AUTHOR]