# The Monte Carlo Method, Scenario Method - International Financial Management

## Monte Carlo method

Monte Carlo simulation is the most complicated, but also the most correct way of assessing and accounting for risks when justifying an investment decision. It allows to build an imitation model with indefinite values ​​of parameters and, knowing the probabilistic distributions of the parameters of a particular project, and also the correlation (correlation) between parameter changes, to obtain the distribution of the efficiency criterion of the analyzed investment project. The enlarged algorithm of the simulation procedure is shown in Fig. 6.9.

Fig. 6.9. Algorithm for risk analysis in the Monte Carlo model

The Monte Carlo method most fully characterizes the whole range of uncertainties with which a real investment project may collide, and through the constraints initially set allows us to take into account all the information available to the project analyst. Practical implementation of this method is possible only with the use of computer programs that allow describing predictive models and calculating a large number of random scenarios.

## Scenario Method

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This method, as a rule, is used to analyze the risks of investment projects that have a foreseeable (reasonable) number of options, graphically depicted usually in the form of a so-called decision tree.

Decision trees are diagrams that allow you to visually examine various alternatives, as well as the likelihood of their implementation. Here each variant of actions or events is represented by a separate branch leading to the subsequent branches, reflecting further actions or possible events. The decision tree is constructed in such a way as to show the full range of alternatives and events that can occur under all analyzed conditions.

The value of the decision tree is determined by the ability to conduct a logical analysis with it, allowing you to analyze a complete strategy that takes into account all possible options before the company chooses one of them. Consider the example and use it to show how the decision tree can be used to make decisions under uncertainty.

Example. The international company is considering whether it is expedient for it to develop a new product and to go out with it to the world market. The development costs are estimated at 180 thousand euros, and there is a probability of 0.75 that the development will be successful and 0.25 - fail.

If the analyzed project is completed, the following results are projected:

1) with a probability of 0.4 the project will be very successful, and the profit in this case will be 540 thousand euro;

2) with a probability of 0.3 the average success of the project is predicted, which will ensure a profit of 100 thousand euros;

3) with a probability of 0.3 the project "fails", which will bring the company losses of 400 thousand euros.

The corresponding decision tree, allowing to make an informed decision on the implementation of the project in question, is shown in Fig. 6.10.

The total expected profit from product development is, as follows from the above decision tree, 49 500 euros. If you follow the theory of decision-making, then it is advisable to develop the product in question. However, this does not mean that the profit of 49500 euros is guaranteed. The calculations only show that if the probabilities of the events are set correctly and the solution is repeated many times, the average profit will be 49 500 euros.

Fig. 6.10. Decision tree for justifying the adoption of the project

Unfortunately, the solutions in this case will not be repeated many times, as the losses incurred by the company can cause it to leave the business before it has the opportunity to repeat the previous decision. Therefore, when deciding whether to develop a given product, managers may prefer to analyze the following probability distribution:

 Exodus, thousand euros Probability Losses of 400 thousand euros 0.225 Losses of 180 thousand euros 0.25 Profit of 100 thousand euros 0.225 Profit 540 thousand euros 0.3

After studying this information, managers can reasonably decide that this project is too risky, since the probability of losses is close to 0.5.

Thus, the decision tree is an effective method of identifying all possible alternative actions and their interdependencies. This approach is particularly useful in establishing the probability distribution when the researcher deals with a large number of different combinations of events.

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