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Lecture 26

Estimating Covariance Matrices

Introduction

Sample covariance measurements (using sample data to find out how two populations move with respect to one another) are susceptible to variation over time. Additionally, the accuracy of a covariance matrix decreases as you increase the number of variables without correspondingly increasing the sample size. Thankfully there exist estimation techniques that given sample data will output a more stable population covariance. This notebook goes over how we would set up and use scikit-learn's covariance estimation functions, and why it is beneficial to use them. The main takeaway of this lecture is that when covariance volatility is a concern, it becomes a necessity to use estimation techniques to accurately analyze and forecast data.

A main motivator for this lecture is Ledoit and Wolf's 'Honey I shrunk the covariance matrix paper'.

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