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

1. Introduction Forecasting financial markets is a sophisticated fusion of quantitative precision and global economic nuance. In this quest, the Monte Carlo simulation stands out as a premier statistical instrument, guiding our understanding of future stock prices. Named after the famous Monte Carlo Casino in Monaco, this method doesn't bank on luck but is rooted in rigorous probabilistic modelling. Imagine orchestrating thousands of experiments in a controlled environment, with each one unfolding a different story of stock price movement. That's the power of the Monte Carlo simulation. In this article, our approach is twofold: We delve into historical and implied volatilities — their distinctions, implications, and relevance We employ the Monte Carlo simulation using both volatility measures to extrapolate potential future paths Central to our analysis will be the derivation of probabilities . By observing the simulation's multitude of potential outcomes, we'll compute the likelihood