2020
DOI: 10.1002/for.2650
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Short‐run wavelet‐based covariance regimes for applied portfolio management

Abstract: Decisions on ass et allocations are often determined by covariance estimates from historical market data. In this paper, we introduce a wavelet-based portfolio algorithm, distinguishing between newly embedded news and long-run information that has already been fully absorbed by the market. Exploiting the wavelet decomposition into short-and long-run covariance regimes, we introduce an approach to focus on particular covariance components. Using generated data, we demonstrate that short-run covariance regimes c… Show more

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Cited by 6 publications
(4 citation statements)
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“…We show that the different investment planning horizons can change the portfolio strategies as Ibragimov et al (2011) and Chakrabarty et al (2015). A view that focuses on short fluctuations, with the use of dealing strategies in short-term scales, can also result in a portfolio with considerable profits as Zhang et al (2016), Baralis et al (2017) and Berger and Gençay (2019) suggests.…”
Section: Final Remarksmentioning
confidence: 92%
See 2 more Smart Citations
“…We show that the different investment planning horizons can change the portfolio strategies as Ibragimov et al (2011) and Chakrabarty et al (2015). A view that focuses on short fluctuations, with the use of dealing strategies in short-term scales, can also result in a portfolio with considerable profits as Zhang et al (2016), Baralis et al (2017) and Berger and Gençay (2019) suggests.…”
Section: Final Remarksmentioning
confidence: 92%
“…( 2016 ), Baralis et al. ( 2017 ) and Berger and Gençay ( 2019 ) suggests.…”
Section: Final Remarksmentioning
confidence: 94%
See 1 more Smart Citation
“…3. Another interesting approach is to distinguish newly embedded news from (less relevant) long-run information by decomposing return series via wavelet transformation (see Berger and Gençay, 2020).…”
Section: Notesmentioning
confidence: 99%