Estimating Project Volatility and Developing Decision Support System in Real Options Analysis
Type of DegreeDissertation
Industrial and Systems Engineering
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Today’s uncertain world requires firms to have a system in place that can analyze the flexibility of their projects. Real options are utilized frequently to quantify the benefits of taking a particular risk. The real options valuation process provides a methodology to measure the value of flexibility, and it assists the decision makers in making the optimal investment decision. The goal of this research is to develop the methodology for improving the real options application in actual capital investment decision making. The Reverse Monte Carlo Simulation model (RMCS), which combines Monte Carlo simulation and the stochastic processes, is developed as a new volatility estimation method for risky projects. Compared to previous simulation methods, RMCS results in more accurate volatility. Then a volatility revision processes based on the previous volatility estimation processes are proposed. A Bayesian revision process is suggested to estimate the new volatility when the initial volatility has been estimated by Monte Carlo simulation. Since specific cases that use typical types of Bayesian conjugate processes are hard to find in the real world, a Dirichlet conjugate process is applied to estimate posterior distributions of the future cash flows. After estimating the new distributions of the cash flows, the revised volatility can be computed using the RMCS approach. Finally, a new early decision rule is developed in order to make real options more useful. This rule concentrates on maximizing the expected future project value. Under the new decision rule, an expected future value of the currently exercised option and the expected future option value are compared in order to determine the best exercise timing. An early decision map for “waiting,” “early exercise,” and “early divest” over the entire option life is developed to automate the decision in case some variables are revised in the future. The map indicates that increasing volatility enlarges the “waiting” area while decreasing volatility shrinks the “waiting” area. A simulation is applied to validate the newly developed decision rule by comparing the benefit of the early exercise rule and the volatility revision during the option life. The new decision rule is found to be useful in maximizing the expected profit of the delayed investment because the proposed decision model results in better than or equal to the current decisions model.