Corporate and Project Finance Modeling

Chapter 19Adding Probabilistic Risk Analysis and Time Series Equations to Financial Models

The remainder of the risk analysis addressed in this chapter and in Chapters 20 through 22 addresses application of mathematics and probability analysis to risk assessment in financial models. One of the differences in using statistical analysis to gauge risk as compared to the methods discussed in Chapters 13 to 18 is that the mathematics and probability analysis does not depend as much as the prior methods do on the subjective opinions of people with respect to the assumption of how high or how low some variable may move in the future. Another difference is that a range of output values for single periods and variations in the output variable across multiple periods is produced from inputs that are expressed in terms of volatility and other mathematical parameters. With these ranges of output values, you can generate probability distributions for any output variable computed in your financial model.

One way to think about stochastic risk analysis is that ranges in variables for sensitivity analysis, break-even analysis, and scenario analysis come from statistical parameters rather than judgmental assessments of potential ranges. Inputs to scenario and sensitivity analysis that drive the analysis for techniques described in Chapters 15 to 18 can be biased upward by managers having a favorable attitude toward an investment concept. The judgment can also be biased downward if managers disagree ...