Monte Carlo Simulation for Stock Price Forecasting: Historical vs Implied Volatility in Python

This article explains how Monte Carlo simulation can be used to forecast stock prices by leveraging both historical and implied volatility measures. It covers the mathematical foundations including drift, volatility terms, and the Black-Scholes model for extracting implied volatility, then demonstrates a Python implementation using libraries like yfinance and scipy. The simulation generates thousands of potential price paths to derive probability distributions for future stock prices.