Monte Carlo Simulation — Risk Modelling & Scenario Analysis

I will design and build a Monte Carlo simulation model for your risk quantification or decision analysis problem — defining stochastic inputs with appropriate probability distributions, running a sufficient number of simulations for convergence, calculating output statistics (mean, percentiles, probability of exceeding threshold), producing distribution plots and sensitivity analysis, and delivering a Python or R simulation with a documented model and results report. Monte Carlo simulation models built without correlation between dependent inputs, insufficient simulation runs for tail percentile stability, or incorrect distribution assumptions produce risk estimates that are directionally correct but quantitatively misleading.

The simulation covers input distribution fitting or specification with justification, correlation matrix for dependent inputs, simulation run (10,000+ iterations for stable percentile estimates), output distribution statistics, tornado chart for input sensitivity, probability of ruin or threshold exceedance, and a documented simulation notebook your team can re-run with updated parameters.

Designed for project risk managers, financial analysts, operations researchers, actuaries, and business strategists quantifying uncertainty in complex decisions.