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Hurst exponents for short time series.

Jingchao Qi1, Huijie Yang

  • 1Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 7, 2012
PubMed
Summary
This summary is machine-generated.

A new method, the balanced estimator of diffusion entropy, reliably detects scaling in short time series. This approach successfully analyzed stock market data, revealing patterns linked to significant market events.

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Area of Science:

  • Quantitative Finance
  • Statistical Analysis
  • Time Series Analysis

Background:

  • Detecting scaling properties in short time series is challenging.
  • Existing methods may lack reliability for limited data lengths.

Purpose of the Study:

  • To introduce a novel method for quantitative scaling detection in short time series.
  • To validate the method's effectiveness using artificial and real-world financial data.

Main Methods:

  • Proposed the balanced estimator of diffusion entropy (BEdE).
  • Applied BEdE to artificial fractional Brownian motions.
  • Analyzed the Shanghai Stock Index and 134 individual stocks from the Shanghai Stock Exchange.

Main Results:

  • BEdE accurately identifies scaling properties in time series as short as ~10^2.
  • Scaling exponents differed significantly between stock catalogs and individual stocks.
  • Analysis of the Shanghai Stock Index revealed scaling evolution patterns correlated with historical market events.

Conclusions:

  • The balanced estimator of diffusion entropy offers a reliable tool for analyzing short time series.
  • The method provides insights into the dynamics of financial markets, linking scaling behaviors to significant events.