It is a well-known fact that macro-economic conditions, such as prices of commodities (e.g. oil, cement and steel) affect the cost of construction projects. In a volatile market environment, highway agencies often pass such risk to contractors using fixed-price contracts. In turn, the contractors respond by adding premiums in bid prices. If the contractors overprice the risk, the price of fixed-price contract could exceed the price of the contract with adjustment clauses. Consequently, highway agencies have the opportunity to design a contract that not only reduces the future risk of exposure, but also reduces the initial contract price. The main goal of this dissertation is to investigate the impact of commodity price risk on construction cost and the optimal risk hedging of such risks using price adjustment clauses. More specifically, the objective of the dissertation is to develop models that can help highway agencies manage commodity price risks. In this dissertation, a weighted least square regression model is used to estimate the risk premium; both univariate and vector time series models are estimated and applied to simulate changes in commodity prices over time, including the effect of correlation; while the genetic algorithm is used as a solution approach to a multi-objective optimization formulation. The data set used in this dissertation consists of TxDOT bidding data, market-based data including New York Mercantile Exchange (NYMEX) future options data, and Engineering News-Record (ENR) material cost index data. The results of this dissertation suggest that the optimal risk mitigation actions are conditional on owners' risk preferences, correlation among the prices of commodities, and volatility of the market.