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The near-extreme density of intraday log-returns

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  • Mauro Politi
  • Nicolas Millot
  • Anirban Chakraborti

Abstract

The extreme event statistics plays a very important role in the theory and practice of time series analysis. The reassembly of classical theoretical results is often undermined by non-stationarity and dependence between increments. Furthermore, the convergence to the limit distributions can be slow, requiring a huge amount of records to obtain significant statistics, and thus limiting its practical applications. Focussing, instead, on the closely related density of "near-extremes" -- the distance between a record and the maximal value -- can render the statistical methods to be more suitable in the practical applications and/or validations of models. We apply this recently proposed method in the empirical validation of an adapted financial market model of the intraday market fluctuations.

Suggested Citation

  • Mauro Politi & Nicolas Millot & Anirban Chakraborti, 2011. "The near-extreme density of intraday log-returns," Papers 1106.0039, arXiv.org.
  • Handle: RePEc:arx:papers:1106.0039
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    File URL: http://arxiv.org/pdf/1106.0039
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    References listed on IDEAS

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    1. Austin Gerig & Javier Vicente & Miguel A. Fuentes, 2009. "Model for Non-Gaussian Intraday Stock Returns," Papers 0906.3841, arXiv.org, revised Dec 2009.
    2. Politi, Mauro & Scalas, Enrico, 2008. "Fitting the empirical distribution of intertrade durations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2025-2034.
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