Portfolio Risk Simulator: Monte Carlo Modeling for Market Risk

Context

Most personal finance tools show static charts that fail to capture the randomness of markets. Beginners struggle to understand volatility, drawdowns, Value at Risk (VaR), and how uncertainty compounds over time. I built a web-based portfolio simulator that uses Geometric Brownian Motion (GBM), Monte Carlo simulations, and multi-asset correlation modeling to make portfolio risk intuitive and interactive for real users.

Problem

How do you help people understand portfolio risk in a world where asset prices move randomly, correlations shift, and volatility clusters? How do you visualize uncertainty, model future price paths, and explain concepts like VaR, CVaR, and probability of loss in a simple, accessible way?

Approach

I used stochastic calculus to derive GBM, built Monte Carlo simulation pipelines in Python, modeled multi-asset correlation using Cholesky decomposition, and wrapped everything in a Streamlit web interface. The goal was to turn complex quant finance concepts into a clean, user-friendly tool that generates realistic simulations and interpretable risk metrics.

Frameworks

Geometric Brownian MotionMonte Carlo SimulationStochastic CalculusCholesky DecompositionRisk ModelingValue at Risk (VaR)Conditional VaRPortfolio TheoryPython Analytics

Implementation

  • •Derived GBM using Ito’s Lemma and implemented it in discrete-time form
  • •Built Monte Carlo simulation engine for multi-asset portfolios
  • •Used Cholesky decomposition to model cross-asset correlations
  • •Integrated risk metrics including VaR, CVaR, volatility, and drawdown
  • •Designed an interactive Streamlit interface for user inputs and visualization
  • •Added automated interpretation text to explain results to non-quants

Outcomes

  • ✓Created a working risk simulator with realistic price path generation
  • ✓Helped beginners understand stochastic processes through visualization
  • ✓Enabled users to model portfolios with multiple assets and correlations
  • ✓Built a foundation for future extensions like optimization and stress testing
  • ✓Received positive feedback for simplifying complex quantitative ideas

Learnings

  • →Visualization dramatically improves understanding of stochastic modeling
  • →Correlation structure is as important as volatility in portfolio behavior
  • →GBM is a useful approximation but must be supplemented with stress testing
  • →Users value interpretation as much as raw simulation output
  • →Quantitative tools become powerful when paired with intuitive UX