Subject. This article deals with economic and mathematical models of investment projects in various sectors of the economy, developed when making a decision to invest in a project. Objectives. The article aims to develop methodological approaches to the construction of probabilistic economic and mathematical models of investment projects using the Monte Carlo method. Methods. For the study, I used analysis and synthesis, forecasting and modeling, the Monte Carlo method, and generalization. Sixteen author-developed investment projects in various sectors of the economy served as the basis for the study. Results. Probabilistic economic and mathematical models of investment projects are a more reliable tool for forecasting and analyzing project risks. The use of probabilistic models helps calculate the magnitude of those risks for which previously only qualitative (verbal) characteristics were available, such as the risk of loss of liquidity (the probability of a cash gap). Probabilistic models meet the requirements for digital twins of production systems. The Monte Carlo method is currently the only available method for constructing probabilistic economic and mathematical models of investment projects. Using the method does not require complex programming and data interpretation; calculations can be performed on low-power public computers. It is advisable to set the initial data (factors of influence) as the base value of the factor and its standard deviation within the normal distribution. The resulting probability distribution functions of key project performance indicators also have the form of a normal probability density distribution. Conclusions and Relevance. New methodological approaches to the use of the Monte Carlo method for constructing probabilistic models of investment projects help implement another method of quantitative analysis of risk projects, helping draw conclusions about the financial sustainability of the project that were previously not available without the use of this method. The results of the study can be used by financial analysts when developing or examining economic and mathematical models in order to determine the likelihood of obtaining the estimated economic efficiency of the project, as well as calculating the likelihood of a cash gap occurring in the investment or operational phase of the project.
Keywords: economic and mathematical modeling, probabilistic models, project risk analysis, Monte Carlo method
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