In this study, a probabilistic simulation model is used to investigate the impact of uncertainty and risk in future cash flow and forecasting models using random distributed input to generate an output distribution of discounted cash flow. There are a number of commercial packages that run simulation; however, during this process a software tool has been developed in support of this work as an application for ship investment analysis and decision support.

A financial evaluation helps an investor to take rational decision about an analyzed investment, whereas an economic analysis broadens this perspective to include effects of the investment imposed on other stakeholders and the environment.

An investment can be evaluated with traditional payback period, or considering time value of money with discounted cash flow (DCF), net present value (NPV), internal rate of return (IRR) and profitability index (PI).

In the traditional sensitivity analysis of cash-flow components, the effect of change in only one variable is considered at a time, where the method to measure risk is to determine several different scenarios for probable results of discounted cash flow (DCF) or net present value (NPV) to justify a realization of the project.

In contrast a simulation method, however, improves on this by looking at the impact of many variables changing at the same time, where a probabilistic simulation model is using statistical distributions as input to incorporate uncertainty and risk. The simulation produces a distribution of the possible outcomes from the simulation, and then a detailed statistical description for the variables of interest and the probability of different outcomes in each scenario is achieved.

The probabilistic simulation method can be summarized as follows:- Step 1: Creating a parametric model, y=f(x_1, x_2,..., x_n )
- Step 2: Generation of random input set of data, x_(1,i), x_(2,i),..., x_(n,i)
- Step 3: Calculations and memorizing results as y_i
- Step 4: Repeating steps 2 and 3 for i=1 to n
- Step 5: Analyzing the results using histograms, confidence intervals, other statistic indicators resulting from the simulation, etc.

The shipping sector is highly cyclical and sensitive to market fluctuations, and consequently the charter rates and ship prices fluctuate correspondingly. The uncertainty and risk include the market, operation, general economic development, and the financial consequences of a change in governing variables.

The demand history for an item is a time series in which it is convenient to distinguish several types of components. The variable components may be classed as:

- Trend: The variation is monotonic over the interval of interest, perhaps because of a low-frequency cycle.
- Cycles: The pattern of variation is reproduced, in some predictable way, over and over.
- Noise: Random variation, including any variation that is not predicted, except in terms of a statistical distribution function.

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