Motivation

Early in my quant finance journey, I learned various time series analysis techniques and how to use them but I failed to develop a deeper understanding of how the pieces fit together. I struggled to see the bigger picture of why we use certain models vs others, or how these models build on each other's weaknesses. The underlying purpose for employing these techniques eluded me for too long. That is, until I came to understand this:

Time series analysis attempts to understand the past and predict the future - Michael Halls Moore [Quantstart.com]

By developing our time series analysis (TSA) skillset we are better able to understand what has already happened, and make better, more profitable, predictions of the future. Example applications include predicting future asset returns, future correlations/covariances, and future volatility.

This post is inspired by the great work Michael Halls Moore has done on his blog, Quantstart, especially his series on TSA. I thought translating some of his work to Python could help others who are less familiar with R. I have also adapted code from other bloggers as well. See References.

This article is a living document. I will update it with corrections as needed and more useful information as time passes.

Before we begin let's import our Python libraries.