Member-only story
In my previous blog post I went into detail about lags and autocorrelation coefficients in time series data, and that post can be read here:- https://tracyrenee61.medium.com/how-to-calculate-autocorrelation-for-a-time-series-problem-in-excel-56f7d97ee823
In this post I intend to discuss a random walk using a Monte Carlo simulation, which will include the key concepts that I covered in the last post.
A Monte Carlo random walk is a statistical technique used to model and analyse systems that evolve over time in a random manner. It uses random sampling and probabilistic methods to simulate the path of a particle or object that makes successive random steps. This technique is used in various fields, such as physics, finance or biology.
The key concepts of the random walk are:-
- A random walk describes a path consisting of a succession of random steps.
- Monte Carlo methods use random sampling to compute numerical results. By simulating a large number of random walks, you can estimate the properties being estimated.
In order to demonstrate the Monte Carlo random walk, I have created one in Excel, as seen below:-
