New Approach to Estimating the Standard Deviations of Lognormal Cost Variables in the Monte Carlo Analysis of Construction Risks

If soundly conducted, risk assessment could yield considerable savings for project investors. Monte Carlo simulation (MCS) has been widely embraced by risk management guides as an instrumental tool for this purpose. This research aims to develop a new method to improve the rigor of MCS by establishing the link between parameter estimation and assessment of individual risk sources. The method is validated by virtue of its predictive power for the likelihood of a project being successful in securing investors. Eight Taiwanese sewerage build-operate-transfer projects are investigated. Compared with the discounted cash flow approach, this new method can provide a more accurate prediction using the expert’s assessment as input of financial impact and occurrence likelihood of individual risks. This finding furnishes solid empirical evidence for the value MCS might add to project appraisal.