Simulating Geometric Brownian Motion in Python | Stochastic Calculus for Quants

In this tutorial we will learn how to simulate a well-known stochastic process called geometric Brownian motion. This code can be found on my website and is implemented in Python. The mathematic notation and explanations are from Steven Shreve's book Stochastic Calculus for Finance II. We will not be describing or explaining what the stochastic differential equation (SDE) means or how to understand its dynamics using Ito calculus. This will be on the agenda for the following video. ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube... ★ A data driven path to getting a job in Quant Finance https://www.quantpykit.com/ ★ QuantPy GitHub Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise.