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.
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Notes
- 1.
Bauwens and Laurent (2005) demonstrate that the one-step and two-step methods provide very similar estimates.
- 2.
- 3.
We use the least asymmetric wavelet with length L = 8, denoted as LA(8).
- 4.
- 5.
The number of zeros between filter coefficients is \(2^{j-1} - 1\), i.e., for the filter at the first stage, we have no zeros, and for the second stage there is just one zero between each coefficient; hence the width of the filter is 15.
- 6.
Oil (Light Crude) is traded on the New York Mercantile Exchange (NYMEX) platform, gold is traded on the Commodity Exchange, Inc. (COMEX), a division of NYMEX, and the S&P 500 is traded at the CME in Chicago. All data were acquired from Tick Data, Inc.
- 7.
The CME introduced the Globex(R) electronic trading platform in December 2006 and began to offer nearly continuous trading.
- 8.
US Business Cycle Expansions and Contractions, NBER, accessed April 5, 2013 (http://www.nber.org/cycles.html).
- 9.
- 10.
For the sake of clarity in the plot, we report monthly correlations, computed on monthly price time series.
- 11.
While the wavelet method is superior to the other two methods in terms of dynamic correlation analysis, we employ the other two methods as a benchmark.
- 12.
For the sake of brevity, we do not report confidence intervals for all estimates. These results are available from the authors upon request.
- 13.
On an annual basis, there was only a small decrease in 2011, as shown in Table 3.
- 14.
Additional information on the role of investors’ beliefs can be found in Ben-David and Hirshleifer (2012).
- 15.
Connolly et al. (2007) study the importance of time-varying uncertainty on asset correlation that subsequently influences the availability of diversification benefits.
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Acknowledgements
We benefited from valuable comments we received from Abu Amin, Ladislav Krištoufek, Brian Lucey, Paresh Narayan, Lucjan Orlowski, Perry Sadorsky, Yi-Ming Wei, and Yue-Jun Zhang. The usual disclaimer applies. The support from the Czech Science Foundation (GAČR) under Grants GA13-24313S and GA14-24129S is gratefully acknowledged.
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Baruník, J., Kočenda, E., Vacha, L. (2014). Wavelet-Based Correlation Analysis of the Key Traded Assets. In: Gallegati, M., Semmler, W. (eds) Wavelet Applications in Economics and Finance. Dynamic Modeling and Econometrics in Economics and Finance, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-07061-2_8
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