Macroeconomic conditions, such as commodity prices, affect the cost of construction projects. In a volatile market environment, contractors respond by adding premiums in bid prices when highway agencies pass such risk on to contractors by using fixed-price contracts. How much of the commodity cost risk should highway agencies pass on to contractors? More specifically, this study aims to investigate the impact of correlation among commodity prices on optimal risk-hedging decisions. A weighted least-squares regression model is used to estimate the risk premium; both univariate time series and vector time series models are estimated and applied to simulate changes in commodity prices over time, including the effect of correlation. A genetic algorithm is used as a solution approach to a multiobjective optimization problem (cost versus future risk exposure). In a case study, project cost risks are shown to be significantly underestimated if correlations are not accounted for.