Simulating Stock Price in Excel

In this video, I demonstrate three distinct Monte Carlo methods for simulating stock prices, moving from a simple stochastic random walk to more advanced Geometric Brownian Motion (GBM) models.

Using real-world historical data from Google (GOOG), we walk through the step-by-step process of calculating the core components of quantitative finance: risk-free rates, annualized volatility, and expected returns. Whether you are looking to price derivatives, assess Value at Risk (VaR), or simply understand market probability distributions, this tutorial provides a practical, spreadsheet-based framework for financial data analytics.

What we cover:

Building a Stochastic Random Walk model based on Efficient Market Hypothesis. Implementing Geometric Brownian Motion (GBM) for derivatives pricing. Comparing Risk-Neutral vs. Expected Return (Drift) simulations. Visualizing results using price paths and statistical histograms.

Download the workbook used here: https://github.com/mjmacarty/stock_prices *For more free resources visit:* https://alphabench.com/resources.html