Wavelet power spectrum analysis of ETF’s tracking error
ISSN: 1526-5943
Article publication date: 25 January 2022
Issue publication date: 2 March 2022
Abstract
Purpose
This study examines the tracking error (TE) of a sample of sector exchange traded funds (ETFs) using spectral techniques.
Design/methodology/approach
TE is examined by computing its power spectrum using the wavelet transform. The wavelet transform maps the TE time series from the time domain to the time–frequency domain. Albeit the wavelet transform is a more complicated mathematical tool compared with the Fourier transform, it also has important advantages such as that it allows to analyze non-stationary data and to detect transient behavior.
Findings
Results show that changes in the TE of a sample of sector ETFs are captured by the wavelet transform. Moreover, the authors also find that the wavelet coherence function can be used as a measure of TE in the time–frequency domain.
Originality/value
The study shows that the wavelet coherence function can be used as a reliable measure of TE.
Keywords
Acknowledgements
The authors thank Héctor Torres Aponte. The authors also extend our gratitude to the University of Puerto Rico, Río Piedras Campus Office of Research and Graduate Studies Institutional Research Fund (FIPI, 20FIP3490018.00).
Citation
Nieves-González, A., Rodríguez, J. and Vega Vilca, J. (2022), "Wavelet power spectrum analysis of ETF’s tracking error", Journal of Risk Finance, Vol. 23 No. 2, pp. 121-138. https://doi.org/10.1108/JRF-04-2021-0058
Publisher
:Emerald Publishing Limited
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