Making Real Options Valuation a Real Option

Risk and Reward Street Crossing

When it comes to valuing projects in highly uncertain business environments, real options valuation (ROV) isn’t popular as a management tool despite having long been touted as a best practice in academic circles. For example, in a 2004 Harvard Business Review article entitled “Making Real Options Really Work,” Alexander van Putten and Ian MacMillan explored the pros and cons associated with deploying real options as a valuation model. “If this fundamental option discipline is not baked into every option project, you are not investing, you are gambling,” they concluded after noting that real options are an essential complement to discounted cash flow (DCF) analysis because they allow managers to “capture the considerable value of being able to ruthlessly abandon floundering projects before making major investments.”

Simply put, ROV provides managers with the freedom required to make better choices. And yet, this highly touted valuation tool remains underutilized by today’s business practitioners. Seeking to understand why industry has failed to adopt this identified best practice, we interviewed valuation specialists in finance, consulting, and the mining industry. We found that the hesitancy to deploy ROV stems from the perceived complexity of doing so.

This article aims to help more industry players see ROV as, well, a real and valuable option by highlighting the case for using real options in highly uncertain business environments and offering recommendations on how to effectively deploy ROV (along with a link to a user-friendly modelling software tool that has been designed to simplify the process).

The Value of Real Options Valuation

Industry relies heavily on static DCF techniques—such as a project’s net present value (NPV) or internal rate of return (IRR)—which implicitly assume that the only decision that matters is the initial decision to invest. In the real world, however, managers must actively oversee projects within a dynamic operating environment and continuously make decisions related to whether or not they should expand or contract a project, abandon one, or simply wait for more information before acting. ROV supports this need for constant decision making, and is therefore particularly applicable for commodity-producing firms, where uncertainty is especially high due to changing market prices and where managers have a substantial ability to respond to this uncertainty by delaying investment until optimal market conditions of the produced commodity are reached.

“For all their theoretical attractiveness as a way to value growth projects, real options have had a difficult time catching on with managers.”

Nevertheless, as the HBR article noted above pointed out in 2004: “For all their theoretical attractiveness as a way to value growth projects, real options have had a difficult time catching on with managers.” In 2002, a survey of capital budgeting practices revealed that only 1.6 per cent of chief financial officers at Fortune 1000 companies utilized ROV as a valuation tool, while NPV and IRR were frequently deployed by 85.1 per cent and 76.7 per cent, respectively.

Jump ahead two decades, and nothing much has changed, according to our interviews with valuation specialists. We found the poor adoption of ROV occurs for three key reasons:

  • First, there is a lack of managerial expertise and knowledge
  • Second, the ROV computation process is highly complex and typically viewed as a “black box”
  • Third, there are communication issues because the outcomes of ROV are not easily understood without detailed knowledge of the underlying concepts.

Although our interview found experts from a Tier-1 mining company considered ROV essential for modelling the optionality of future investment decisions, we also found that DCF models were predominantly used. When ROV was deployed, firm managers also admitted to outsourcing more complex ROV valuations to external consulting companies.

To overcome these limitations and promote the use of real options, we present three recommendations in Figure 1.

Figure 1: Promoting the Use of Real Options

To make these recommendations actionable, we have developed a new ROV software tool to value commodity-producing projects such as mines or refineries. We have also created and made available an interactive and intuitive dashboard. Figure 2 presents a segment which demonstrates identical inputs to a standard DCF model but expands upon this valuation approach by using ROV to embed the volatility of the underlying asset, increasing the accessibility of this approach and providing greater asset performance metrics.

Figure 2: Segment of ROV Interactive Dashboard

To grasp the additional value of managerial flexibility provided by ROV, consider a privately held firm faced with the opportunity to develop a mine that produces gold. The owner possesses the flexibility to begin mining within the next 10 years, and it is estimated that the gold deposit will take 20 years to be depleted after mining begins. Because the value of the gold deposit depends on the market price of gold, the manager may wish to delay investment until a future date (an American option) when market prices appreciate. Adding volatility (15.27 per cent based on past gold prices) allows ROV users to consider a wide range of possible gold paths and incorporate the implicit value of deferring the investment.

Using Monte Carlo simulation to value the project through both the static DCF and dynamic ROV approaches yields substantially different results because NPV fails to take the gold price uncertainty into account. This gold price volatility results in a higher project value with ROV, consistent with the well-documented evidence that NPV systematically undervalues risky investments (van Putten and MacMillan, 2004). ROV dynamically hedges against bad outcomes, only signaling for investment when the immediate benefits outweigh the likelihood of future unfavourable gold prices. The positive waiting option value (ROV minus NPV) increases the total expected return, which better reflects real-world managerial decision making. The software dashboard displays the gold trigger price at which the investment should commence and shows additional real options metrics. Overall, our real options valuation enables managers to quantify inherent uncertainty and identify flexibility, which in return increases understanding of the investment decision.

Real options can add value without adding complexity. Our Monte Carlo simulation method approximates ROV by determining the optimal exercise strategy, enabling users to model complex real options problems that include multiple stochastic variables, early exercise rights, and operational flexibility. In other words, by following our three recommendations and utilizing our new software tool, practitioners can experience the benefits of real options while letting a user-friendly interface do the hard work.

  1. Aspinall et al., “LSMRealOptions: Value American and Real Options Through LSM Simulation,” CRAN, 2021, accessed January 12, 2023,
  2. A. Ryan and G. P. Ryan, “Capital Budgeting Practices of the Fortune 1000: How Have Things Changed?” Journal of Business and Management 8, no. 4 (2002): 355–364.
  3. van Putten and I. MacMillan, “Making Real Options Really Work,” Harvard Business Review 82, no. 12 (2004): 134–141.