An investigation into the effects and effectiveness of correlation network filtration methods with financial returns.

ABSTRACT: When studying financial markets, we often look at estimating a correlation matrix from asset returns. These tend to be noisy, with many more dimensions than samples, so often the resulting correlation matrix is filtered. Popular methods to do this include the minimum spanning tree, planar maximally filtered graph and the triangulated maximally filtered graph, which involve using the correlation network as the adjacency matrix of a graph and then using tools from graph theory. These assume the data fits some form of shape. We do not necessarily have a reason to believe that the data does fit into this shape, and there have been few empirical investigations comparing how the methods perform. In this paper we look at how the filtered networks are changed from the original networks using stock

SUBMITTER: Millington T 

PROVIDER: S-EPMC9451073 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature