Abstract: This paper deals with the robust portfolio optimization problems with linear transaction costs. The model is based on the worst-case Conditional Value-at-Risk (CVaR) risk measure and uncertainty for the portfolio return distribution. We show that the robust portfolio optimization problems with box uncertainty  and ellipsoidal uncertainty structures can be reformulated as linear programming and second-order cone programming, respectively. Moreover, we illustrate our model by an example of market data simulation.