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A top–bottom price approach to understanding financial fluctuations

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  • Rivera-Castro, Miguel A.
  • Miranda, José G.V.
  • Borges, Ernesto P.
  • Cajueiro, Daniel O.
  • Andrade, Roberto F.S.

Abstract

The presence of sequences of top and bottom (TB) events in financial series is investigated for the purpose of characterizing such switching points. They clearly mark a change in the trend of rising or falling prices of assets to the opposite tendency, are of crucial importance for the players’ decision and also for the market stability. Previous attempts to characterize switching points have been based on the behavior of the volatility and on the definition of microtrends. The approach used herein is based on the smoothing of the original data with a Gaussian kernel. The events are identified by the magnitude of the difference of the extreme prices, by the time lag between the corresponding events (waiting time), and by the time interval between events with a minimal magnitude (return time). Results from the analysis of the inter day Dow Jones Industrial Average index (DJIA) from 1928 to 2011 are discussed. q-Gaussian functions with power law tails are found to provide a very accurate description of a class of measures obtained from the series statistics.

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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 391 (2012)
Issue (Month): 4 ()
Pages: 1489-1496

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Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1489-1496

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

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Keywords: Switching point; Return interval; Waiting time; Smoothing kernel;

References

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Citations

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Cited by:
  1. Han, Ruokang & Takahashi, Taiki, 2012. "Psychophysics of time perception and valuation in temporal discounting of gain and loss," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6568-6576.
  2. Pyrlik, Vladimir, 2013. "Autoregressive conditional duration as a model for financial market crashes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6041-6051.

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