Pricing real options based on linear loss functions and conditional value at risk

The main purpose of this paper is to expand real option analysis out of the realm of pure financial option pricing techniques. To overcome many of the well-known concerns by adopting the financial option pricing techniques for modeling real options problems such as replicating portfolio concept, geometric Brownian motion as underlying stochastic process, and estimating project volatility, we propose an alternative real option valuation based on the loss function approach. The option value determined by the loss function approach is equivalent to the expected value of perfect information (EVPI) in decision analysis. It basically sets the upper bound of risk premium to pay in retaining the options. In practice, many firms utilize the concept of Value at Risk to manage their portfolio risk. If a firm sets a target VAR, then we may be able to link this VAR in refining the actual risk premium to pay in hedging the risk embedded in the investment. With this practice in mind, we present a logic to figure out an appropriate amount of real option premium to pay for a given level of risk tolerance. A comprehensive example is presented to demonstrate the computational procedures as well as economic interpretations on the outcomes.