Open AccessModel and Empirical Research on Risk Assessment Based on Second-Order Monte Carlo Simulation
Author(s) -
Xiaohong Gao,
Jian Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1813/1/012015
Subject(s) - monte carlo method , credibility , computer science , monte carlo molecular modeling , monte carlo integration , quasi monte carlo method , hybrid monte carlo , markov chain monte carlo , mathematics , statistics , political science , law
First order Monte Carlo simulation simplifies the processing of input variables, while the analysis results can not meet the needs of major decisions. Second order Monte Carlo simulation divides the input variables into uncertainty and variability, which can help decision makers understand the impact of different input variables on the results and the credibility of the evaluation results. On the basic of analysing the two methods, an enterprise’s investment project was taken as an example; using point estimation method, first-order Monte Carlo simulation and second-order Monte Carlo methods respectively for risk assessment and comparison. Finally, conclusions and recommendations are given for using second-order Monte Carlo simulation for risk assessment of Investment Project Decision-making.