**Monte Carlo Simulation: Assessing A Reasonable Degree of Certainty**

**A summary By Kevin Muvunyi****Summary:**

In their article “Monte Carlo Simulation: Assessing A Reasonable
Degree of Certainty”, Daily and Solis apply the Monte Carlo simulation
technique to two hypothetical scenarios that seek to determine future financial
outcomes with a certain degree of confidence by examining the benefits and
drawbacks of the methodology. According to the authors, Monte Carlo simulation
has a large scope of applicability in various fields and more so in financial analysis
hence the interest therein.

In their analysis, Daily and Solis examine a simple lost profits analysis and
then a more complex construction delay claim requiring the evaluation of lost
profits.
First, they begin by tackling each scenario based on known facts, evidence, and
assumptions followed by a repeat of the same process but this time using the
Monte Carlo simulation. In the case of the lost profits analysis, the
researchers first utilize single inputs as part of their assumption based
analysis to get the lost profits values. They then proceed to use the Monte
Carlo technique with the help of the Microsoft Excel based RISK program, whereby
with the use of probability distributions they are able to run 10000 iterations
to get final results. What the authors were able to discern in this particular
case is that the lost profits value in both instances were approximately
similar. In the second case of the construction delay claim, the researchers
repeated the same processes but this time around due to the complexity of the
scenarios they were inclined to use multiple inputs, thus, there was significant
material differences between the results of the two methodologies, namely the
Monte Carlo simulation and the assumption based technique. Ultimately, Daily
and Solis conclude that the Monte
Carlo simulation is able to have a material effect on the ultimate outcome or
no material effect at all in regards to financial analysis. Nonetheless, they
stress the fact that in both scenarios Monte Carlo provided them with helpful
statistics regarding the possible outcomes of their analyses.

**Critique:**

Although the
article provides two practical examples of how the Monte Carlo simulation can
be applied to real world scenarios it nonetheless fails to clearly demonstrate
the drawbacks of the technique in a financial analysis context.

Source: http://eds.b.ebscohost.com.ezproxy.mercyhurst.edu/ehost/pdfviewer/pdfviewer?vid=6&sid=f08e7165-fad1-4f17-945c-45825adca828%40pdc-v-sessmgr01

How much did the additional variables affect the accuracy of the Monte Carlo simulations? Similarly, how did they adjust for the assumption model not really have standardized values for the more complex scenario?

ReplyDeleteAlso, what numbers did they use for the single digit inputs? If they're using random numbers for the variables which variables are they representing?

ReplyDeleteI think it would have been more interesting to run this study comparatively to a different technique in order to show the benefit of a monte carlo simulation.

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