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Value-at-risk (VaR) is a risk measure of potential loss on a specific portfolio. The main uses of VaR are in risk management and financial reporting. Researchers are continuously looking for new and efficient ways to evaluate VaR, and the 2008 financial crisis has given further impetus to finding new and reliable ways of evaluating and using VaR. In this study, the authors use genetic algorithm (GA) to evaluate VaR and compare the results with conventional VaR techniques.

In essence, the authors propose two modifications to the standard GA: normalized population selection and strict population selection. For a typical set of simulation, eight chromosomes were used each with eight stored values, and the authors get eight values for VaR.

The experiments using data from four different market indices show that by adjusting the volatility, the VaR computed using GA is more conservative as compared to those computed using Monte Carlo simulation.

The proposed methodology is designed for VaR computation only. This could be generalized for other applications.

This is achieved with much less cost of computation, and hence, the proposed methodology could be a viable practical approach for computing VaR.

The proposed methodology is simple and, at the same time, novel that could have far-reaching impact on practitioners.

Although the mean‐variance portfolio selection model has been investigated in the literature, the difficulty associated with the application of the model when dealing with large‐scale problems is limited. The aim of this paper is to close the gap by using the quadratic risk (standard deviation risk) function and finite iteration technique to remove difficulties in quadratic programming.

Using van de Panne' approach, this paper proposes a finite technique to optimize large‐scale portfolio selection problem.

The proposal of parallel algorithm structure to the model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process.

The proposal of parallel algorithm structure to the mean‐variance portfolio selection model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process. An empirical example that illustrates the application and benefits of the method is provided.