A Comparison of Hurst Exponent Estimators in Long-range Dependent Curve Time Series-Reference-Cited by-同舟云学术

A Comparison of Hurst Exponent Estimators in Long-range Dependent Curve Time Series

Published:2020-01-02 Issue:1 Volume:12 Page:
ISSN:1941-1928
Container-title:Journal of Time Series Econometrics
language:en
Short-container-title:

Author:

Shang Han Lin¹²^ORCID

Affiliation:

1. Australian National University , Research School of Finance, Actuarial Studies and Statistics , Level 4, Building 26C, Kingsley St, Acton , Canberra , Australian Capital Territory, 2601 , Australia

2. Department of Actuarial Studies and Business Analytics , Level 7, 4 Eastern Rd , Macquarie University , NSW, 2109 , Australia

Abstract

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 (ARFIMA) models, we compare finite-sample bias, variance and mean square error among some time- and frequency-domain Hurst exponent estimators and make our recommendations.

Publisher

Walter de Gruyter GmbH

Subject

Economics and Econometrics

Link

https://www.degruyter.com/document/doi/10.1515/jtse-2019-0009/pdf

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