Collar Option Model for Managing the Cost Overrun Caused by Change Orders

Abstract

Abstract:Effective change order management is very important in maintaining the financial sustainability of various stakeholders related to construction projects by minimizing cost overruns. In this study, we propose a zero-cost risk management approach based on the collar option model in order to control for the loss caused by change orders, the main cause of cost overruns in construction projects. We apply this model to actual projects for empirical analysis. The analysis, based on 237 projects, indicates that insurance buyers benefit from the collar option model in 46% of the cases, while insurance sellers do so in 53% of the cases. In most cases, the insurance buyer is the owner. According to the model, the owner experiences a loss when the cost overrun caused by change orders is lower than what was expected. In such cases, it is appropriate to conclude that the loss is not caused by the collar option model, but by the absence of additional revenue. However, the insurance seller suffers a loss if the cost overrun is higher than the strike price of the call option. Thus, the insurance seller needs to have expertise in construction management.

Putting context to numbers: a geotechnical risk trajectory to cost overrun extremism

“…Mathematical models have however been scantly used in the literature to infer causality in relation to project cost overruns. The techniques which have being used to analyse cost overrun in a limited number of older and more contemporary studies include: Linear modelling techniques such as regression modelling, networking and data mining techniques such as artificial neural networks, heuristics based models such as case based reasoning, stochastic techniques such as monte-carlo simulations; and logic based methods such as binary logistic modelling and fuzzy logic (Love, 2002;Trost and Oberlender, 2003;Attala and Hegazy, 2003;Ahiagu Dugbai et al, 2014;Lee andKim, 2015 andEl-Kholy, 2015) Typically, older studies such as Trost and Oberlender (2003) as well as Attala and Hegazy (2003) have used linear modelling techniques, based on regression analysis, to analyse cause-effect relationships in explaining recorded cost overruns in projects, and further tested the validity of these models with respect to their use in decision making for future projects, at specified levels of confidence. A more recent study by El-Kholy (2015) generated a regression based model, while comparing its predictive capacity to a Case Based Reasoning (CBR) model for similar data sets derived from 30 projects.…”

“…Ahiagu Dugbai et al (2014) used data mining techniques based on artificial neural networks, to analyse the complexity of non-linear interactions amongst quantitative project variables such as compensation events, project duration, as well as qualitative information on tendering method, location, project type, fluctuation measure and project's delivery partner. Lee and Kim (2015) used monte-carlo simulations to analyse the statistical distribution of change orders issued during the construction period, which lead to significant cost overruns. Love et al (2013) developed a probabilistic log-logistic distribution of cost overruns for 49 road projects (new roads including upgrades and elevated highways) in relation to rework occasioned by errors and omissions in contract documentation, leading to cost overruns.…”

Comparison of Black-Scholes Models Using Historical Volatility and Garch Volatility in Collar Strategy as Hedging Efforts for Towr and Tbig Stocks

“…Isynuwardhana and Surur (2018) conducted a study and analysis of the return rates of option contracts using long straddle and long-strangle strategies and concluded that the long straddle strategy was significantly more profitable. According to Lee and Kim (2015), the collar strategy can be used as a hedging strategy. Basson, Van den Berg, and Van Vuuren (2018) stated that the zero-cost collar (ZCC) and long butterfly (LB) strategies yielded the best returns on an index with moderate volatility and good performance.…”