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Wavelet-Based Correlation Analysis of the Key Traded Assets

In: Wavelet Applications in Economics and Finance

Author

Listed:
  • Jozef Baruník

    (Academy of Sciences of the Czech Republic
    Charles University)

  • Evžen Kočenda

    (Charles University and the Czech Academy of Sciences
    CESifo
    The William Davidson Institute at the University of Michigan Business School
    CEPR)

  • Lukas Vacha

    (Academy of Sciences of the Czech Republic
    Charles University)

Abstract

This chapter reveals the time-frequency dynamics of the dependence among key traded assets—gold, oil, and stocks, in the long run, over a period of 26 years. Using both intra-day and daily data and employing a variety of methodologies, including a novel time-frequency approach combining wavelet-based correlation analysis with high-frequency data, we provide interesting insights into the dynamic behavior of the studied assets. We account for structural breaks and reveal a radical change in correlations after 2007–2008 in terms of time-frequency behavior. Our results confirm different levels of dependence at various investment horizons indicating heterogeneity in stock market participants’ behavior, which has not been documented previously. While these key assets formerly had the potential to serve as items in a well-diversified portfolio, the events of 2007–2008 changed this situation dramatically.

Suggested Citation

  • Jozef Baruník & Evžen Kočenda & Lukas Vacha, 2014. "Wavelet-Based Correlation Analysis of the Key Traded Assets," Dynamic Modeling and Econometrics in Economics and Finance, in: Marco Gallegati & Willi Semmler (ed.), Wavelet Applications in Economics and Finance, edition 127, pages 157-183, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-07061-2_8
    DOI: 10.1007/978-3-319-07061-2_8
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