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arXiv · 2003.08787

A comparison of Hurst exponent estimators in long-range dependent curve time series

Han Lin Shang
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Abstract

The Hurst exponent is the simplest numerical summary of self-similar long-range dependent stochastic processes. We consider the estimation of Hurst exponent in long-range dependent curve time series. Our estimation method begins by constructing an estimate of the long-run covariance function, which we use, via dynamic functional principal component analysis, in estimating the orthonormal functions spanning the dominant sub-space of functional time series. Within the context of functional autoregressive fractionally integrated moving average models, we compare finite-sample bias, variance and mean square error among some time- and frequency-domain Hurst exponent estimators and make our recommendations.

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BibTeXRIS

Han Lin Shang. 2020-03-17. A comparison of Hurst exponent estimators in long-range dependent curve time series. https://doi.org/10.1515/jtse-2019-0009

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