Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series
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| Title: | Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series |
|---|---|
| Authors: | Morales, Raffaello1 raffo.morales@gmail.com, Di Matteo, T.1, Gramatica, Ruggero1, Aste, Tomaso2,3 |
| Source: | Physica A. Jun2012, Vol. 391 Issue 11, p3180-3189. 10p. |
| Subjects: | Financial markets, Financial crises, Exponents, Generalization, Time series analysis, Distribution (Economic theory), Prices, Business enterprises |
| Abstract: | Abstract: 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 2007–2008 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. [Copyright &y& Elsevier] |
| Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 73525819 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Morales%2C+Raffaello%22">Morales, Raffaello</searchLink><relatesTo>1</relatesTo><i> raffo.morales@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Di+Matteo%2C+T%2E%22">Di Matteo, T.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gramatica%2C+Ruggero%22">Gramatica, Ruggero</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Aste%2C+Tomaso%22">Aste, Tomaso</searchLink><relatesTo>2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Jun2012, Vol. 391 Issue 11, p3180-3189. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Financial+markets%22">Financial markets</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+crises%22">Financial crises</searchLink><br /><searchLink fieldCode="DE" term="%22Exponents%22">Exponents</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Economic+theory%29%22">Distribution (Economic theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Prices%22">Prices</searchLink><br /><searchLink fieldCode="DE" term="%22Business+enterprises%22">Business enterprises</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: 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 2007–2008 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. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.physa.2012.01.004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 3180 Subjects: – SubjectFull: Financial markets Type: general – SubjectFull: Financial crises Type: general – SubjectFull: Exponents Type: general – SubjectFull: Generalization Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Distribution (Economic theory) Type: general – SubjectFull: Prices Type: general – SubjectFull: Business enterprises Type: general Titles: – TitleFull: Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Morales, Raffaello – PersonEntity: Name: NameFull: Di Matteo, T. – PersonEntity: Name: NameFull: Gramatica, Ruggero – PersonEntity: Name: NameFull: Aste, Tomaso IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 03784371 Numbering: – Type: volume Value: 391 – Type: issue Value: 11 Titles: – TitleFull: Physica A Type: main |
| ResultId | 1 |
