Wavelet power spectrum analysis of ETF’s tracking error

Aniel Nieves-González, Javier Rodríguez and José Vega Vilca

Journal of Risk Finance, 2022, vol. 23, issue 2, 121-138

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: Spectral analysis; Wavelet transform; Fourier transform; Exchange traded fund (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jrfpps:jrf-04-2021-0058

DOI: 10.1108/JRF-04-2021-0058

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