Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/62110
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dc.contributor.authorLee, Zhen Ting-
dc.date.accessioned2015-01-21T03:23:47Z-
dc.date.available2015-01-21T03:23:47Z-
dc.date.copyright2014en_US
dc.date.issued2014-
dc.identifier.urihttp://hdl.handle.net/10356/62110-
dc.description.abstractCross correlation is widely used in many areas to measure mutual influence between variables. However, most cross correlation metrics measure the mutual information between a pair of variables, when in fact these can also be correlated with a third variable. In this FYP, we define a partial cross correlation that measures the mutual information that is exclusively between a pair of variables. We apply this partial correlation metric to the cross section of S&P 500 stocks, and test for systematic differences between the linear and partial correlations. We also address the question of whether the partial correlation can be distorted because an incomplete cross section of variables is used. By computing the partial correlations within toy networks, we realized that the distortions carry signatures of the network topologies. We further explain how these signatures can be understood within the framework of the controllability of complex networks.en_US
dc.format.extent51 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Science::General::Economic and business aspectsen_US
dc.titlePartial cross correlations and its applications to financial time series clusteringen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorCheong Siew Annen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.degreeBachelor of Science in Physicsen_US
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Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)
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