Portfolio selection of uncertain random returns based on value at risk

Value at risk is a device to measure the maximum possible loss when right tail distribution ignored. Since uncertain random variables provide tool deal with phenomena in which uncertainty and randomness simultaneously exist, this paper proposes computational approach for value of variables. In fact, chance can be expressed based on expectation respect probability functions. Thus, distributions are computed via Monte Carlo simulation. And consequently, obtained statistical quintile index. As an application finance, portfolio selection problems returns optimized mean–value models.