3.4 Risk Assessment
This type of analysis assigns a probability distribution to each of the critical variables
of the sensitivity analysis, defined in a precise range of values around the best
estimate, used as the base case, in order to recalculate the expected values of
financial and economic performance indicators. The probability distribution for
each variable may be derived from different sources, such as experimental data,
distributions found in the literature for similar cases or consultation with experts.
Obviously, if the process of generating the distributions is unreliable, the risk
assessment is unreliable as well. However, in its simplest design (e.g. triangular
distribution), this step is always feasible and represents an important improvement in
the understanding of the project’s strengths and weaknesses as compared with the
base case.
Having established the probability distributions for the critical variables, it is
possible to proceed with the calculation of the probability distribution of the FRR or
the NPV of the project. For this purpose, the use of the Monte Carlo method is
suggested, which requires a simple computation software. The method consists on
the repeated random extraction of a set of values for the critical variables, taken
within the respective defined intervals, and then the calculation of the performance
indices for the project (FRR or NPV) resulting from each set of the extracted values.
By repeating this procedure for a large enough number of extractions, one can obtain
a predefined convergence of the calculation as the probability distribution of the IRR
or NPV.
The values obtained enable the analyst to infer significant judgements about the
level of risk of the project. The result of the Monte Carlo drawings, expressed in
terms of the probability distribution or cumulated probability of the IRR or the NPV
in the resulting interval of values, provides more comprehensive information about
the risk profile of a project. The cumulated probability curve (or a table of values)
assesses the project risk, for example, verifying whether the cumulative probability
for a given value of NPV or IRR is higher or lower than a reference value that is
considered to be critical.
3.5 Normalisation of Project’s Flows
In this section, the information about flows in the projects (CAPEX, OPEX, DECEX
and revenues) are presented in a “normalised format”. This step should help to assess
the flows per unit of production. This new presentation of the data is useful in the
way that it allows to compare with other similar projects. This type of analysis is
performed by sector. The data per sector are presented in the following subsections.
3 Comparative Financial Analysis of Marine Multipurpose Platforms Projects. . .
45
This type of analysis assigns a probability distribution to each of the critical variables
of the sensitivity analysis, defined in a precise range of values around the best
estimate, used as the base case, in order to recalculate the expected values of
financial and economic performance indicators. The probability distribution for
each variable may be derived from different sources, such as experimental data,
distributions found in the literature for similar cases or consultation with experts.
Obviously, if the process of generating the distributions is unreliable, the risk
assessment is unreliable as well. However, in its simplest design (e.g. triangular
distribution), this step is always feasible and represents an important improvement in
the understanding of the project’s strengths and weaknesses as compared with the
base case.
Having established the probability distributions for the critical variables, it is
possible to proceed with the calculation of the probability distribution of the FRR or
the NPV of the project. For this purpose, the use of the Monte Carlo method is
suggested, which requires a simple computation software. The method consists on
the repeated random extraction of a set of values for the critical variables, taken
within the respective defined intervals, and then the calculation of the performance
indices for the project (FRR or NPV) resulting from each set of the extracted values.
By repeating this procedure for a large enough number of extractions, one can obtain
a predefined convergence of the calculation as the probability distribution of the IRR
or NPV.
The values obtained enable the analyst to infer significant judgements about the
level of risk of the project. The result of the Monte Carlo drawings, expressed in
terms of the probability distribution or cumulated probability of the IRR or the NPV
in the resulting interval of values, provides more comprehensive information about
the risk profile of a project. The cumulated probability curve (or a table of values)
assesses the project risk, for example, verifying whether the cumulative probability
for a given value of NPV or IRR is higher or lower than a reference value that is
considered to be critical.
3.5 Normalisation of Project’s Flows
In this section, the information about flows in the projects (CAPEX, OPEX, DECEX
and revenues) are presented in a “normalised format”. This step should help to assess
the flows per unit of production. This new presentation of the data is useful in the
way that it allows to compare with other similar projects. This type of analysis is
performed by sector. The data per sector are presented in the following subsections.
3 Comparative Financial Analysis of Marine Multipurpose Platforms Projects. . .
45