Portfolio Demo Project

Monte Carlo Risk Simulator

An interactive demonstration of quantitative risk analysis using Monte Carlo and Latin Hypercube sampling methods. Built to showcase statistical programming and financial modeling capabilities.

⚠️

Educational Purpose Only

This tool was created as a portfolio project to demonstrate programming skills in statistical analysis, React development, and quantitative modeling. While I am a professional risk management specialist who creates and maintains Probabilistic Risk Assessment (PRA) software for Idaho National Laboratory, I am not a licensed financial advisor. This simulator should not be used for actual investment decisions or financial planning. Always consult qualified financial professionals for investment advice.

Technical Skills

  • • Monte Carlo simulation algorithms
  • • Statistical distribution sampling
  • • Interactive React components
  • • Real-time data visualization

Mathematical Concepts

  • • Probability distributions
  • • Value at Risk (VaR) calculation
  • • Statistical moments analysis
  • • Latin Hypercube Sampling

Business Applications

  • • Portfolio risk modeling
  • • Project cost estimation
  • • Revenue forecasting
  • • Uncertainty quantification

Monte Carlo Risk Analyzer

Advanced risk simulation using Monte Carlo and Latin Hypercube Sampling methods. Quantitative analysis for portfolio management, project planning, and operational risk assessment.

Monte CarloLatin HypercubeStatistical AnalysisRisk MetricsVaR Calculation
Portfolio Value at Risk
Multi-asset portfolio risk including stocks, bonds, commodities, and cryptocurrency
Formula: stockReturn * stockWeight + bondReturn * bondWeight + commodityReturn * commodityWeight + cryptoReturn * cryptoWeight

Adjust Distribution Parameters:

Stock Market Return (%)
normal
Normal: avg 8.5, spread ±16.2
-50100
0.150
Bond Market Return (%)
normal
Normal: avg 3.2, spread ±4.8
-50100
0.150
Commodity Return (%)
normal
Normal: avg 5.1, spread ±22.4
-50100
0.150
Cryptocurrency Return (%)
normal
Normal: avg 12.8, spread ±65.3
-50100
0.1130.6
Stock Allocation
uniform
Uniform: equally likely between 0.35 and 0.6
-49.6550.35
0.3100.6
Bond Allocation
uniform
Uniform: equally likely between 0.15 and 0.35
-49.8550.15
0.175100.35
Commodity Allocation
uniform
Uniform: equally likely between 0.05 and 0.15
-49.9550.05
0.075100.15
Crypto Allocation
uniform
Uniform: equally likely between 0.02 and 0.15
-49.9850.02
0.075100.15
📊 Adjust parameters above and run simulation to see results

Technical Implementation

Monte Carlo Methods

  • • Standard Monte Carlo sampling with pseudo-random numbers
  • • Latin Hypercube Sampling for improved convergence
  • • Box-Muller transform for normal distribution generation
  • • Inverse CDF methods for distribution sampling
  • • Statistical moment calculations (mean, variance, skewness, kurtosis)

Risk Applications

  • • Value at Risk (VaR) calculation for portfolio management
  • • Project cost estimation with uncertainty quantification
  • • Revenue forecasting with multiple risk factors
  • • Operational risk assessment and scenario analysis
  • • Regulatory capital calculation and stress testing

Note: This implementation demonstrates quantitative finance and risk management expertise. Latin Hypercube Sampling provides better coverage of the probability space compared to standard Monte Carlo, especially important for tail risk estimation in financial applications.

Built with React, TypeScript, and Recharts. Source code demonstrates advanced statistical programming, interactive UI development, and mathematical modeling capabilities. Created for portfolio demonstration purposes.