FORECASTING the Stock Market with Python - Geometric Brownian Motion
In this video we'll see how to exploit the Geometric Brownian Motion to simulate a number of future scenarios of the stock market.
From Wikipedia: A geometric Brownian motion (GBM) (also known as exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion (also called a Wiener process) with drift.[1] It is an important example of stochastic processes satisfying a stochastic differential equation (SDE); in particular, it is used in mathematical finance to model stock prices in the Black–Scholes model.
This video can be useful to price financial derivatives, even Bermudan and American option, thanks to the "DIY" discretization of the future time window.
Chapters:
00:00 - Introduction
00:44 - Returns and Volatility
03:29 - Geometric Brownian Motion
10:14 - Final Results