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Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series

Morales, Raffaello; Di Matteo, Tiziana; Gramatica, Ruggero; Aste, Tomaso

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We investigate the use of the Hurst exponent, dynamically computed over a weighted moving time-window, to evaluate the level of stability/instability of financial firms. Financial firms bailed-out as a consequence of the 20072008 credit crisis show a neat increase with time of the generalized Hurst exponent in the period preceding the unfolding of the crisis. Conversely, firms belonging to other market sectors, which suffered the least throughout the crisis, show opposite behaviors. We find...[Show more]

dc.contributor.authorMorales, Raffaello
dc.contributor.authorDi Matteo, Tiziana
dc.contributor.authorGramatica, Ruggero
dc.contributor.authorAste, Tomaso
dc.date.accessioned2015-12-13T22:19:33Z
dc.identifier.issn0378-4371
dc.identifier.urihttp://hdl.handle.net/1885/71861
dc.description.abstractWe investigate the use of the Hurst exponent, dynamically computed over a weighted moving time-window, to evaluate the level of stability/instability of financial firms. Financial firms bailed-out as a consequence of the 20072008 credit crisis show a neat increase with time of the generalized Hurst exponent in the period preceding the unfolding of the crisis. Conversely, firms belonging to other market sectors, which suffered the least throughout the crisis, show opposite behaviors. We find that the multifractality of the bailed-out firms increase at the crisis suggesting that the multi fractal properties of the time series are changing. These findings suggest the possibility of using the scaling behavior as a tool to track the level of stability of a firm. In this paper, we introduce a method to compute the generalized Hurst exponent which assigns larger weights to more recent events with respect to older ones. In this way large fluctuations in the remote past are less likely to influence the recent past. We also investigate the scaling associated with the tails of the log-returns distributions and compare this scaling with the scaling associated with the Hurst exponent, observing that the processes underlying the price dynamics of these firms are truly multi-scaling.
dc.publisherElsevier
dc.sourcePhysica A: Statistical mechanics and its applications
dc.subjectKeywords: Econophysicss; Financial time series; Generalized Hurst exponent; Hurst exponents; Market sectors; Multi-fractal property; Multifractality; Multiscaling; Price dynamics; Scaling behavior; Stability/instability; Time windows; Financial data processing; Ind Econophysics; Generalized Hurst exponent; Multi-scaling analysis
dc.titleDynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume391
dc.date.issued2012
local.identifier.absfor020400 - CONDENSED MATTER PHYSICS
local.identifier.ariespublicationf5625xPUB2920
local.type.statusPublished Version
local.contributor.affiliationMorales, Raffaello, King's College London
local.contributor.affiliationDi Matteo, Tiziana, King's College London
local.contributor.affiliationGramatica, Ruggero, King's College London
local.contributor.affiliationAste, Tomaso, College of Physical and Mathematical Sciences, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.issue11
local.bibliographicCitation.startpage3180
local.bibliographicCitation.lastpage3189
local.identifier.doi10.1016/j.physa.2012.01.004
dc.date.updated2016-02-24T09:03:48Z
local.identifier.scopusID2-s2.0-84858076406
local.identifier.thomsonID000302693600081
CollectionsANU Research Publications

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