Many investors use optimization to determine their optimal investment portfolio. Unfortunately, optimal portfolios are sensitive to changing input parameters, i.e., they are not robust. Traditional robust optimization approaches aim for an optimal and robust portfolio which, ideally, is the...

In practical applications, and especially in high dimensional models, it is common that not all covariances are known due to, for example, data limitations. In such situations, the covariance matrix must be completed in order to use the statisti-cal or stochastic model at hand. The already...