Monday, November 12, 2018

Monte Carlo Method in risk analysis for investment projects

By Victor Platona and Andreea Constantinescua

Investment projects worth over € 50 million that are financed with EU support must meet certain conditions, including criteria related to the size of the risk. For major projects, risk analysis indicates whether risks have been taken into account in estimating the costs. In this study, the authors propose a risk estimation method, Monte Carlo method, to be applied in a standardized way on these investment projects. In the Monte Carlo method, artificial values of a probabilistic variable are generated through a random uniformly distributed number generator in [0, 1] intervals. The method algorithm is shown in its succession interactive five steps:

Step 1: Creating a parametric model, y = f(x1, x2, ..., xq);
Step 2: Generation of random input set of data, xi1, xi2, ..., xiq;
Step 3: Effective calculations and memorizing results as yi;
Step 4: Repeating steps 2 and 3 for i = 1 to n (n 5000);
Step 5: Analyzing the results using histograms, confidence intervals, other statistic indicators resulting from the simulation, etc.

The authors used 23 waste management projects and a number of 40 water and wastewater projects, which have been contracted and are under implementation. They then calculated average, standard deviation and relative standard deviation for the two types of projects.

The water supply projects had larger average value of a project is €106.23 million compared to waste management projects average of € 31.7 million. They then estimated the risk of exceeding the project value.

This showed that the average is € 50.75 million, slightly lower than the chosen project analysis, € 51.76 million. The asymmetry is 0.05 - which shows a slightly expanded distribution to the right and the flattening is -0.1 - indicating a slight flattening compared to normal distribution. The authors also estimated the risk of exceeding the project implementation period. Some of their over all conclusions included that competitive bidding system can make the offering price lower than the starting price. There are higher chances of exceeding the initially established period due to multiple situations occurring during implementation. Finally that Monte Carlo method is relatively easy to perform and provides important information regarding the risks of investment projects.

The authors laid out a step-by-step way evaluate both the risk of exceeding the project value and the risk of exceeding the project implementation period. If the Monte Carlo method is applied correctly and with accurate inputs it can be useful in evaluating the risks in investments. Like many methods bad data will cause miss leading results that could be very costly in the financial industry.



  1. Did the authors indicate how incomplete the data can be before the monte carlo becomes useless? I don't know if that would come in a percentage form--for example, "I can confidently say I have 37% of the picture"--and some scholar says, "nope, for this to work you have to have at least 60% of the picture." As analysts we have exceptionally incomplete data and I wonder if anyone's decided where we have to cap the incompleteness of our information.

    1. Jillian you brought up several great points! I think one of the reasons monte carlo simulations can be applied to financials so easily is because for the most part the data is there. This is something businesses track all the time. If something is missing the is typically a way to calculate it with an equation. It would be interesting to see in other scenarios what is considered enough data though.

  2. Alyssa,
    The Monte Carlo simulation appears to provide a great foundation for future strategy development. Now that the EU knows the risk of exceeding the project implementation, the EU can make appropriate strategic actions to reduce the risk (e.g., competitive bidding).

  3. In contrast to some of the other papers shown here on Monte Carlo Simulations, the authors here show how simple and effective MC can be on moderately complex problems. It provides some reasonable bounds as well as the possibility for overruns to the investor, in this case the EU.

    1. Harry, I completely agree. MC simulations cant always be applied in situations. However, like I said to Jillian businesses track financials, so for the most part the data is there.

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