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Multiscale Partial Correlation Clustering of Stock Market Returns

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  • Antonis A. Michis

    (Central Bank of Cyprus, 1395 Nicosia, Cyprus)

Abstract

This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

Suggested Citation

  • Antonis A. Michis, 2022. "Multiscale Partial Correlation Clustering of Stock Market Returns," JRFM, MDPI, vol. 15(1), pages 1-22, January.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:1:p:24-:d:720850
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    References listed on IDEAS

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    1. Michis Antonis & Sapatinas Theofanis, 2007. "Wavelet Instruments for Efficiency Gains in Generalized Method of Moment Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(4), pages 1-25, December.
    2. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2018. "Dynamic correlations at different time-scales with empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 534-544.
    3. Joanna Bruzda, 2014. "Forecasting via Wavelet Denoising: The Random Signal Case," Dynamic Modeling and Econometrics in Economics and Finance, in: Marco Gallegati & Willi Semmler (ed.), Wavelet Applications in Economics and Finance, edition 127, pages 187-225, Springer.
    4. Marco Gallegati & Willi Semmler (ed.), 2014. "Wavelet Applications in Economics and Finance," Dynamic Modeling and Econometrics in Economics and Finance, Springer, edition 127, number 978-3-319-07061-2, March.
    5. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
    6. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
    7. Gençay, Ramazan & Signori, Daniele, 2015. "Multi-scale tests for serial correlation," Journal of Econometrics, Elsevier, vol. 184(1), pages 62-80.
    8. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
    9. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2005. "Multiscale systematic risk," Journal of International Money and Finance, Elsevier, vol. 24(1), pages 55-70, February.
    10. Jensen, Mark J., 2000. "An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 361-387, March.
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    Cited by:

    1. Beatrice Franzolini & Alexandros Beskos & Maria De Iorio & Warrick Poklewski Koziell & Karolina Grzeszkiewicz, 2022. "Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market," Papers 2208.00952, arXiv.org, revised May 2023.
    2. Roman Mestre, 2023. "Stock profiling using time–frequency-varying systematic risk measure," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.
    3. Jingying Yang & Guishu Bai & Mei Yan, 2023. "Minimum Residual Sum of Squares Estimation Method for High-Dimensional Partial Correlation Coefficient," Mathematics, MDPI, vol. 11(20), pages 1-22, October.
    4. Antonis A. Michis, 2023. "Precious Metals Comovements in Turbulent Times: COVID-19 and the Ukrainian Conflict," JRFM, MDPI, vol. 16(5), pages 1-18, May.

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