Managing commodity risks in highway contracts : quantifying premiums, accounting for correlations among risk factors, and designing optimal price-adjustment contracts.
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Managing commodity risks in highway contracts : quantifying premiums, accounting for correlations among risk factors, and designing optimal price-adjustment contracts.

Filetype[PDF-1.85 MB]


  • English
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    • Creators:
    • Corporate Creators:
      Texas Transportation Institute
    • Corporate Contributors:
      Texas A & M University
    • Subject/TRT Terms:
    • Publication/ Report Number:
    • Resource Type:
      Tech Report
    • Geographical Coverage:
      Texas
    • Corporate Publisher:
      Southwest Region University Transportation Center (U.S.)
    • Abstract:
      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 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 report 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 report is to develop models that can help highway agencies manage commodity price risks.

      In this report, 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 genetic algorithm is used as a solution approach to a multiobjective

      optimization formulation. The data set used in this report 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 report suggest that the

      optimal risk mitigation actions are conditional on owners’ risk preferences, correlation among the prices of

      c ommodities, and volatility of the market.

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      PDF
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      10727
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