Monte Carlo Simulation

Explanation

Monte Carlo simulation generates thousands (or millions) of random scenarios based on specified probability distributions. In finance, it's used for: option pricing (when closed-form solutions don't exist), portfolio risk analysis (stress testing across market scenarios), retirement planning (will savings last 30 years?), and derivative valuation. Each simulation produces a different outcome based on randomly generated inputs. The aggregate results reveal the probability distribution of outcomes — not just the expected value, but the range and likelihood of extreme outcomes.

How Stoquity Uses This

Stoquity incorporates monte carlo simulation analysis across its portfolio management platform, providing real-time monitoring and AI-powered insights for every portfolio.