Several Sets of Assumptions for the Monte Carlo Simulation for a More Precise Analysis of Enterprise Risk

Kaczmarzyk Jan ()
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Kaczmarzyk Jan: University of Economics in Katowice, Katowice, Poland

Econometrics. Advances in Applied Data Analysis, 2019, vol. 23, issue 4, 80-95

Abstract: The traditional methods of risk quantification include a sensitivity analysis, a scenario analysis and a historical simulation. The true nature of risk factors changes is ignored in the traditional ‘ceteris paribus’ approach to a sensitivity analysis, hence it can be reflected in a scenario analysis and a historical simulation. The most significant disadvantage of a scenario analysis is the limited number of scenarios, whereas a historical simulation depends on historical data availability and adequacy. The Monte Carlo simulation is a clear answer to the limitations of traditional methods. The changes of risk factors reflected in the Monte Carlo simulation are simultaneous, non-linear and interdependent. The most important aspect of this method is the stage of taking up the assumptions. The purpose of the paper is to indicate that considering several reasonable sets of assumptions for the Monte Carlo simulation simultaneously can bring even more comprehensive information about enterprise risk.

Keywords: corporate finance; risk analysis; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: G32 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:eaiada:v:23:y:2019:i:4:p:80-95:n:6

DOI: 10.15611/eada.2019.4.06

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