Monte Carlo Simulations for Resolving Veridical Paradoxes in Forecast Risk Management and Corporate Treasury Applications

Managing forecast risk is key during the treasury management process. The study aims to apply Monte Carlo simulation to solve three classical probabilistic paradoxes and their implementation in corporate treasury management. The article presents the Monte Carlo simulation as an advanced risk management tool in treasury management processes. This method allows a comprehensive risk analysis of financial forecasts, making it possible to assess potential errors in cash flow forecasts and predict the value of corporate treasury growth under various future scenarios. In investment decision-making, Monte Carlo simulation supports evaluating the effectiveness of financial projects by calculating the expected net value and identifying the risks of related investments, allowing for more informed decisions on project implementation. The method finds application in reducing cash flow volatility, which contributes to lowering the cost of capital and increasing the company's value. The simulation also enables more accurate liquidity planning, including forecasting cash availability and determining appropriate financial reserves based on probability distributions. Monte Carlo also supports the management of credit and interest rate risk, allowing simulation of the effects of various economic scenarios on a company's financial obligations. In the context of strategic planning, the method is an extension of decision tree analysis, where subsequent decisions are made based on the results of earlier ones. Creating probabilistic models based on Monte Carlo simulations makes it possible to consider random variables and their impact on key treasury management indicators, such as free cash flow (FCF). Compared to traditional methods, Monte Carlo simulation offers a more detailed and precise approach to risk analysis and decision-making, providing companies with vital information for financial management under uncertainty. The article emphasizes that using Monte Carlo simulation in treasury management not only enhances the effectiveness of risk management but also supports the long-term growth of enterprise value. The entire treasury management process is moving into the future and is based on the prediction of future free cash flows discounted at the cost of capital. We used both numerical method and analytical methods to solve veridical-type paradoxes. We used Monte Carlo simulation as a numerical method. The following analytical methods were used: conditional probability, Bayes' rule, and Bayes' rule with multiple conditions. We solved all the veridical type paradoxes and discovered why such a considerable discussion of Monty Hall's problem appeared in the 90s. We varied Monty Hall problems, using different door counts and prize counts.