Real Option Methodology for Risk Assessment in Asset Management

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Real Option Methodology for Risk Assessment in Asset Management


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A number of approaches are commonly used to manage risk, including conducting visual inspections of existing infrastructure, using design standards with conservative safety factors for new infrastructure, and applying best practices for minimizing risks of project cost and schedule overruns. Research is needed to determine how to build on existing practices to better assess the risks to transportation assets, better quantify consequences of different risks, and better prioritize investments explicitly acknowledging uncertainty in future events.

R = Σ pi × Ci

Risk (R) is generally quantified with the equation above. It is essentially a value for the expected outcome returns of a decision weighted by the probability (p) of each consequence (C) of event i. How do we calculate R if neither p nor C is certain? Do current methods address this effectively?

Investment decisions are widely made using discounted cash flows (DCF). It is assumed, that given a certain decision made in Year 0, Costs and Benefits can be assumed for a number of years to come, i.e. C is known and p is assumed 1 for all i. If the project is considered risky, the discount rate is increased accordingly. However, defining the future in- and outflow of cash with such deterministic certainty is unrealistic. Not only is the consequence (C) uncertain, but also their occurrence. This is because infrastructure is often affected by stochastically occurring events.

We can ignore the uncertainty by using expected values. Imposing an assumed expected value will nevertheless almost certainly lead to arriving at a wrong risk estimation (see figure X). This is called the “flaw of averages” (Savage, 2012). The error due to the “flaw of averages” exponentiates when systems are non-linear because outputs using expected inputs do not equal expected outputs. Ultimately, it can be said that ignoring the uncertainty and the consequent existence of a distribution instead of a deterministic expected value, is a fallacy.

Discrepancies between the forecast and actual costs of road projects, Source: (de Neufville and Scholtes, 2011)

Another approach to compensate for increased risk is overdesigning infrastructure. This reduces the probability of failure to negligent values, but may lead to infrastructure being overly expensive or redundant. This also ignores the fact that infrastructure owners are not passive, but actively observe their condition and relevant external factors and trends that affect the condition level of the infrastructure. Based on this, the fundamental assumptions of DCF do not seem appropriate.

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The ultimate objective is to provide the decision-maker with tools that add value to the decision-making process and improve the robustness of the infrastructure network as a whole. In that sense, novel approaches for the evaluation of risk will be sought to capture the stochastic nature of interdependent infrastructure. A graph theory approach to evaluate criticality of network node failure as shown by Buldyrev and colleagues (2010) may prove interesting for the evaluation of consequences, and thus the real option value for the infrastructure, simulated by network programming methods.

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