Abstract
We describe how networks based on information theory can help measure and visualize systemic risk, enhance diversification, and help price assets. To do this, we first define a distance measure based on the mutual information between asset pairs and use this measure in the construction of minimum spanning trees. The dynamics of the shape and the descriptive statistics of these trees are analyzed in various investment domains. The method provides evidence of regime changes in dependency structures prior to market sell-offs, and as such, it is a potential candidate for monitoring systemic risk. We also provide empirical evidence that the assets that are located towards the center of the network tend to have higher returns. Finally, an investment strategy that utilizes network centrality information is shown to add value historically.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
