Fractal structure of financial high frequency data

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We propose a new method to describe scaling behavior of time series. We introduce an extension of extreme values. Using these extreme values determined by a scale, we define some functions. Moreover, using these functions, we can measure a kind of fractal dimension - fold dimension. In financial high frequency data, observations can occur at varying time intervals. Using these functions, we can analyze non-equidistant data without interpolation or evenly sampling. Further, the problem of choosing the appropriate time scale is avoided. Lastly, these functions are related to a viewpoint of investor whose transaction costs coincide with the spread.

Original languageEnglish
Pages (from-to)13-18
Number of pages6
JournalFractals
Volume10
Issue number1
DOIs
Publication statusPublished - 2002
Externally publishedYes

Fingerprint

High-frequency Data
Fractal Structure
Financial Data
Fractals
Extreme Values
Transaction Costs
Fractal dimension
Scaling Behavior
Fractal Dimension
Time series
Time-varying
Interpolation
Time Scales
Fold
Interpolate
Sampling
Interval
Costs

ASJC Scopus subject areas

  • General
  • Geometry and Topology

Cite this

Fractal structure of financial high frequency data. / Kumagai, Yoshiaki.

In: Fractals, Vol. 10, No. 1, 2002, p. 13-18.

Research output: Contribution to journalArticle

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