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dc.contributor.authorFiedor, Paweł
dc.date.accessioned2015-12-10T11:52:07Z
dc.date.available2015-12-10T11:52:07Z
dc.date.issued2015
dc.identifier.issn0208-6018
dc.identifier.urihttp://hdl.handle.net/11089/15381
dc.description.abstractWe treat financial markets as complex networks. It is commonplace to create a filtered graph (usually a Minimally Spanning Tree) based on an empirical correlation matrix. In our previous studies we have extended this standard methodology by exchanging Pearson’s correlation coefficient with information – theoretic measures of mutual information and mutual information rate, which allow for the inclusion of non-linear relationships. In this study we investigate the time evolution of financial networks, by applying a running window approach. Since information–theoretical measures are slow to converge, we base our analysis on the Hirschfeld-Gebelein-Rényi Maximum Correlation Coefficient, estimated by the Randomized Dependence Coefficient (RDC). It is defined in terms of canonical correlation analysis of random non-linear copula projections. On this basis we create Minimally Spanning Trees for each window moving along the studied time series, and analyse the time evolution of various network characteristics, and their market significance. We apply this procedure to a dataset describing logarithmic stock returns from the Warsaw Stock Exchange for the years between 2006 and 2013, and comment on the findings, their applicability and significance.pl_PL
dc.description.abstractW niniejszym artykule traktujemy rynki finansowe jako sieci złożone. Najczęściej wyznacza się minimalne drzewo rozpinające oparte o empiryczną macierz korelacji. W naszych wcześniejszych badaniach rozszerzyliśmy tę metodologię poprzez zamianę współczynnika korelacji liniowej Pearsona na miary oparte o teorię informacji: informację wzajemną i stopę informacji wzajemnej, co pozwala na uwzględnienie zależności nieliniowych. W niniejszym badaniu zajmujemy się ewolucją sieci finansowych w czasie, przy zastosowaniu mechanizmu przesuwnego okna. Jako że miary oparte o teorię informacji są znane z wolnej zbieżności, opieramy naszą analizę na współczynniku największej korelacji Hirschfelda-Gebeleina-Rényiego, estymowanym przez randomizowany współczynnik zależności (RDC). Jest on definiowany w odniesieniu do analizy korelacji kanonicznych losowych nieliniowych odwzorowań za pomocą kopuł. Na tej podstawie tworzymy minimalnego drzewa rozpinające dla każdego okna przesuwającego się wzdłuż badanych szeregów czasowych, analizujemy ewolucję różnych własności tych sieci w czasie, i ich znaczenie dla badanego rynku. Stosujemy tę procedurę w odniesieniu do zestawu danych opisującego logarytmiczne zwroty cen akcji z Giełdy Papierów Wartościowych w Warszawie z lat pomiędzy 2006 i 2013, komentujemy otrzymane wyniki, możliwości ich praktycznego zastosowania oraz ich znaczenie dla badaczy i analityków.pl_PL
dc.language.isoenpl_PL
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl_PL
dc.relation.ispartofseriesActa Universitatis Lodziensis. Folia Oeconomica;314
dc.subjectfinancial networkspl_PL
dc.subjectnon-linear dependencepl_PL
dc.subjectmaximum correlation coefficientpl_PL
dc.subjectcanonical-correlation analysispl_PL
dc.subjectsieci finansowepl_PL
dc.subjectzależności nieliniowepl_PL
dc.subjectwspółczynnik największej korelacjipl_PL
dc.subjectkorelacja kanonicznapl_PL
dc.titleAnalysis of the Time Evolution of Non-Linear Financial Networkspl_PL
dc.title.alternativeAnaliza ewolucji nieliniowych sieci finansowychpl_PL
dc.typeArticlepl_PL
dc.rights.holder© Copyright by Uniwersytet Łódzki, Łódź 2015pl_PL
dc.page.number[69]-81pl_PL
dc.contributor.authorAffiliationCracow University of Economics, Rakowicka 27, 31-510 Krakówpl_PL
dc.identifier.eissn2353-7663
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dc.contributor.authorEmailPawel.F.Fiedor@ieee.orgpl_PL
dc.identifier.doi10.18778/0208-6018.314.09
dc.relation.volume3pl_PL


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