'Programa de Pos-graduacao em Ciencias Contabeis da UFRJ'
Abstract
Estimation risk is an important topic within the area of risk management. Uncertanties
on the parameter estimates carry on to the ¯nal statistical product, for example to the
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. We address this issue in this paper and propose a semiparametric
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 variability of the Markowitz e±cient
frontier. Simulation experiments are conducted to assess the out-of-sample forecasting
usefulness of the semi-parametric bootstrap methodology.Indisponíve
