Scaling and correlation in financial time series

P. Gopikrishnan*, V. Plerou, Y. Liu, L. A N Amaral, X. Gabaix, H. E. Stanley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

114 Scopus citations

Abstract

We discuss the results of three recent phenomenological studies focussed on understanding the distinctive statistical properties of financial time series - (i) The probability distribution of stock price fluctuations: Stock price fluctuations occur in all magnitudes, in analogy to earthquakes - from tiny fluctuations to very drastic events, such as the crash of 19 October 1987, sometimes referred to as `Black Monday'. The distribution of price fluctuations decays with a power-law tail well outside the Levy stable regime and describes fluctuations that differ by as much as 8 orders of magnitude. In addition, this distribution preserves its functional form for fluctuations on time scales that differ by 3 orders of magnitude, from 1 min up to approximately 10 days. (ii) Correlations in financial time series: While price fluctuations themselves have rapidly decaying correlations, the magnitude of fluctuations measured by either the absolute value or the square of the price fluctuations has correlations that decay as a power-law, persisting for several months. (iii) Volatility and trading activity: We quantify the relation between trading activity - measured by the number of transactions NΔt - and the price change GΔt for a given stock, over a time interval [t,t+Δt]. We find that NΔt displays long-range power-law correlations in time, which leads to the interpretation that the long-range correlations previously found for |GΔt| are connected to those of NΔt.

Original languageEnglish (US)
Pages (from-to)362-373
Number of pages12
JournalPhysica A: Statistical Mechanics and its Applications
Volume287
Issue number3-4
DOIs
StatePublished - Dec 1 2000

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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