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Statistical Inference for Stochastic Processes

, Volume 3, Issue 3, pp 263–276 | Cite as

Semiparametric Bootstrap Approach to Hypothesis Tests and Confidence Intervals for the Hurst Coefficient

  • Peter Hall
  • Wolfgang Härdle
  • Torsten Kleinow
  • Peter Schmidt
Article

Abstract

A major application of rescaled adjusted range analysis (R–S analysis) is to the study of price fluctuations in financial markets. There, the value of the Hurst constant, H, in a time series may be interpreted as an indicator of the irregularity of the price of a commodity, currency or similar quantity. Interval estimation and hypothesis testing for H are central to comparative quantitative analysis. In this paper we propose a new bootstrap, or Monte Carlo, approach to such problems. Traditional bootstrap methods in this context are based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages, autoregressive moving averages and many more. By way of contrast we suggest simulation using a single type of continuous-time process, with its fractal dimension. We provide theoretical justification for this method, and explore its numerical properties and statistical performance by application to real data on commodity prices and exchange rates.

Box-counting method commodity price financial market fractal dimension fractional Brownian motion Gaussian process long-range dependence Monte Carlo R–S analysis self affineness self similarity 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Peter Hall
    • 1
  • Wolfgang Härdle
    • 2
  • Torsten Kleinow
    • 2
  • Peter Schmidt
    • 3
  1. 1.Centre for Mathematics and its ApplicationsAustralian National UniversityCanberraAustralia
  2. 2.Institut für Statistik und ÖkonometrieHumboldt-Universität zu BerlinBerlinGermany
  3. 3.Quantitative ResearchBankgesellschaft Berlin AGGermany

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