Large dimension forecasting models and random singular value spectra

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dc.creatorBouchaud, Jean-Philippe
dc.creatorLaloux, Laurent
dc.creatorMiceli, M. Augusta
dc.creatorPotters, Marc
dc.date2005-12-10
dc.date.accessioned2026-07-07T12:07:41Z
dc.date.available2026-07-07T12:07:41Z
dc.descriptionWe present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random matrices, and gives an explicit expression for the interval where singular values are expected in the absence of any true correlations between the variables under study. Our result can be seen as the natural generalization of the Marcenko-Pastur distribution for the case of rectangular correlation matrices. We illustrate the interest of our method on a set of macroeconomic time series.
dc.description20 pages, 5 figures, submitted to EPJB
dc.identifierhttps://arxiv.org/abs/physics/0512090
dc.identifierhttp://arxiv.org/abs/physics/0512090
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/209054
dc.subjectData Analysis, Statistics and Probability
dc.subjectStatistical Mechanics
dc.subjectStatistical Finance
dc.titleLarge dimension forecasting models and random singular value spectra
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