Estimation risk is an important topic within the area of risk management. Uncertainties regarding parameter estimates carry on to the final statistical product, such as investment strategies, and need to be estimated and accounted for. Unless the exact expressions for the estimators’ variances are known, the product’s variability will be assessed through bootstrap techniques. The present paper addresses this issue, proposing a semi-parametric bootstrap method for reproducing the data, a method which parametrically takes care of all marginal characteristics of the returns data, and also takes care of the dependence structure existing in the data, in a very simple and clever non-parametric way. The technique is applied to the problem of assessing the variability of the Markowitz-efficient frontier. Simulation experiments are conducted to assess the out-of-sample forecasting usefulness of the semi-parametric bootstrap methodology.