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dc.contributor.authorValle, Mauricio A.
dc.contributor.authorLavín, Jaime F.
dc.contributor.authorMagner, Nicolás S.
dc.date.accessioned2021-12-21T12:32:49Z
dc.date.available2021-12-21T12:32:49Z
dc.date.issued2021-10-21
dc.identifier.citationValle, M. A., Lavín, J., Magner, N. S. Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution, Special Issue ”Ising Model: Recent Devel- opements and Exotic Applications”, Entropy, Vol.23(10), 1307. DOI: h ps://doi.org/10.3390/e23101307.es
dc.identifier.issn1099-4300
dc.identifier.other0000-0002-3414-3996es
dc.identifier.otherhttps://orcid.org/0000-0003-1362-2776es
dc.identifier.otherhttps://orcid.org/0000-0003-1380-6938es
dc.identifier.otherhttps://doi.org/10.3390/e23101307
dc.identifier.urihttp://hdl.handle.net/20.500.12254/2169
dc.description.abstractThe financial market is a complex system in which the assets influence each other, causing, among other factors, price interactions and co-movement of returns. Using the Maximum Entropy Principle approach, we analyze the interactions between a selected set of stock assets and equity indices under different high and low return volatility episodes at the 2008 Subprime Crisis and the 2020 COVID-19 outbreak. We carry out an inference process to identify the interactions, in which we implement the a pairwise Ising distribution model describing the first and second moments of the distribution of the discretized returns of each asset. Our results indicate that second-order interactions explain more than 80% of the entropy in the system during the Subprime Crisis and slightly higher than 50% during the COVID-19 outbreak independently of the period of high or low volatility analyzed. The evidence shows that during these periods, slight changes in the second-order interactions are enough to induce large changes in assets correlations but the proportion of positive and negative interactions remains virtually unchanged. Although some interactions change signs, the proportion of these changes are the same period to period, which keeps the system in a ferromagnetic state. These results are similar even when analyzing triadic structures in the signed network of couplingses
dc.language.isoen_USes
dc.publisherMDPIes
dc.relation.ispartofseriesEntropy;23(10)
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subject.otherMaximum entropy principlees
dc.subject.otherFinancial crisises
dc.subject.otherPairwise interactionses
dc.subject.otherFrustrationes
dc.subject.otherKullback-Leibler divergencees
dc.subject.otherReturn volatilitieses
dc.titleEquity market description under high and low volatility regimes using maximum entropy pairwise distributiones
dc.typeArtículoes


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Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
Except where otherwise noted, this item's license is described as Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)