English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In the last years efforts in econophysics have been shifted to study hownetwork theory can facilitate understanding of complex financial markets. Mainpart of these efforts is the study of correlation-based hierarchical networks.This is somewhat surprising as the underlying assumptions of research lookingat financial markets is that they behave chaotically. In fact it's common foreconophysicists to estimate maximal Lyapunov exponent for log returns of agiven financial asset to confirm that prices behave chaotically. Chaoticbehaviour is only displayed by dynamical systems which are either non-linear orinfinite-dimensional. Therefore it seems that non-linearity is an importantpart of financial markets, which is proved by numerous studies confirmingfinancial markets display significant non-linear behaviour, yet network theoryis used to study them using almost exclusively correlations and partialcorrelations, which are inherently dealing with linear dependencies only. Inthis paper we introduce a way to incorporate non-linear dynamics anddependencies into hierarchical networks to study financial markets using mutualinformation and its dynamical extension: the mutual information rate. Weestimate it using multidimensional Lempel-Ziv complexity and then convert itinto an Euclidean metric in order to find appropriate topological structure ofnetworks modelling financial markets. We show that this approach leads todifferent results than correlation-based approach used in most studies, on thebasis of 15 biggest companies listed on Warsaw Stock Exchange in the period of2009-2012 and 91 companies listed on NYSE100 between 2003 and 2013, usingminimal spanning trees and planar maximally filtered graphs.

Mutual Information Rate-Based Networks in Financial Markets