Generalized Autoregressive Conditional Heteroscedasticity Model to Quantify and Forecast Uncertainty in the Price of Asphalt Cement

M. Ilbeigi, D. Castro-Lacouture, A. Joukar

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Significant uncertainty in the price of asphalt cement is a serious challenge to estimating proper cost in projects for transportation agencies. Because of price uncertainty, transportation agencies may face risks such as price speculation, inflated bids, very short-term price guarantees, and too few bidders for projects. Currently, various risk-management strategies, such as price-adjustment clauses, are used to control the consequences of uncertainty associated with the price of materials. Before implementing any risk-management strategy, it is necessary to ensure whether it is the proper time to use the strategy by measuring, analyzing, and forecasting the material price uncertainty. Transportation agencies need to regularly monitor the level of uncertainty in the price of asphalt cement to keep decisions about implementing their risk-management strategies current. However, there is little knowledge about quantifying and forecasting uncertainty in the price of asphalt cement. This gap in knowledge makes it difficult to recognize the proper time to implement risk-management strategies. The research objective of this paper is to measure, model, and forecast asphalt-cement price uncertainty. Autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) time-series models are used to quantify and forecast the uncertainties in the price of asphalt cement. The results of this study can help transportation agencies accurately recognize the proper time to implement risk-management strategies.

Original languageEnglish
Article number04017026
JournalJournal of Management in Engineering
Volume33
Issue number5
DOIs
StatePublished - 1 Sep 2017

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