Estimators for Long Range Dependence: An Empirical Study

By William S. Rea, Les Oxley, Marco Reale, Jennifer A. Brown. 2009

513 citations recorded by the source corpus.

jennifer.brown@cantebury.ac.nzAbstract:We present the results of a simulation study into the properties of 12diﬀerent estimators of the Hurst parameter, H, or the fractional integra-tion parameter, d, in long memory time series. We compare and contrasttheir performance on simulated Fractional Gaussian Noises and fractionallyintegrated series with lengths between 100 and 10,000 data points and Hvalues between 0.55 and 0.90 or d values between 0.05 and 0.40. We applyall 12 estimators to the Campito Mountain data and estimate the accuracyof their estimates using the Beran goodness of ﬁt test for long memory timeseries.MCS code: 37M10Keywords and phrases: Strong dependence, Global dependence, Longrange dependence, Hurst parameter estimators.

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Citations in the corpus

On Hurst exponent estimation under heavy-tailed distributions (2010, methodology): “The rescaled range scales as (R/S)υ ≈ cυ , where c is a finite constant independent of υ (Di Matteo, 2007; Taqqu et al., 1995).”
Commodity futures and market efficiency (2013, methodology): “For comparison of both time and frequency domain estimators, see Beran (1994), Taqqu et al. (1995), Taqqu and Teverovsky (1996), Robinson (1995), Geweke and Porter-Hudak (1983), Di Matteo et al. (2003), Di Matteo (2007), Barunik and Kristoufek (2010) and Teverovsky et al. (1999).”
Multi-scaling in finance (2007, background): “There has been a proliferation of papers proposing different techniques and providing comparison studies between them (Taqqu et al. 1995).”
Some stylized facts of the Bitcoin market (2017, methodology): “For a survey on the different methods for estimating long range dependences see [51] and [43].”
Statistical analysis of hydroclimatic time series: Uncertainty and insights (2007, methodology): “on the largest scales), even the detection of LTP is highly uncertain when dealing with time series with short length [Taqqu et al., 1995].”
Limit order books (2010, background): “However, the performance of such estimators on empirical data, which might not conform to the estimators’ assumptions, varies considerably (Xu et al. 2005, Rea et al. 2009).”
Detecting persistence of meteorological drought via the Hurst exponent (2015, methodology): “Several techniques have been proposed in the literature for calculating the H exponent (Geweke and Porter, 1983; Lo 1991; Peng et al., 1994; Peters 1994; Taqqu et al., 1995; Kendziorski 1999).”