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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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.
References
Aggarwal R, Lucey BM (2007) Psychological barriers in gold prices? Rev Financ Econ 16(2):217–230
Aguiar-Conraria L, Martins M, Soares MJ (2012) The yield curve and the macro-economy across time and frequencies. J Econ Dyn Control 36:1950–1970
Andersen T, Benzoni L (2007) Realized volatility. In: Andersen T, Davis R, Kreiss J, Mikosch T (eds) Handbook of financial time series. Springer, Berlin
Andersen T, Bollerslev T, Diebold F, Labys P (2003) Modeling and forecasting realized volatility. Econometrica 71(2):579–625
Andrews DW (1993) Tests for parameter instability and structural change with unknown change point. Econometrica 61:821—856
Andrews DW, Ploberger W (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62:1383–1414
Bandi F, Russell J (2006) Volatility. In: Birge J, Linetsky V (eds) Handbook of financial engineering. Elsevier, Amsterdam
Barndorff-Nielsen O, Shephard N (2004) Econometric analysis of realized covariation: high frequency based covariance, regression, and correlation in financial economics. Econometrica 72(3):885–925
Bartram SM, Bodnar GM (2009) No place to hide: the global crisis in equity markets in 2008/2009. J Int Money Finance 28(8):1246–1292
Baur DG, Lucey BM (2010) Is gold a hedge or a safe haven? an analysis of stocks, bonds and gold. Financ Rev 45(2):217–229
Bauwens L, Laurent S (2005) A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models. J Bus Econ Stat 23(3):346–354
Ben-David I, Hirshleifer D (2012) Are investors really reluctant to realize their losses? trading responses to past returns and the disposition effect. Rev Financ Stud 25(8):2485–2532
Bollerslev T (1990) Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch approach. Rev Econ Stat 72(3):498–505
Büyükşahin B, Robe MA (2013) Speculation, commodities and cross-market linkages. J Int Money Finance 42:38–70
Carlson M (2007) A brief history of the 1987 stock market crash with a discussion of the federal reserve response. Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board
Cashin P, McDermott C, Scott A (1999) The myth of co-moving commodity prices. IMF working paper WP/99/169 international monetary fund, Washington
Cashin P, McDermott CJ, Scott A (2002) Booms and slumps in world commodity prices. J Dev Econ 69(1):277–296
Chui C (1992) An inroduction to wavelets. Academic, New York
Connolly RA, Stivers C, Sun L (2007) Commonality in the time-variation of stock–stock and stock–bond return comovements. J Financ Mark 10(2):192–218
Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia
Engle R (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20(3):339–350
Engle RF, Sheppard K (2001) Theoretical and empirical properties of dynamic conditional correlation multivariate garch. Technical report, National Bureau of Economic Research
Faÿ G, Moulines E, Roueff F, Taqqu M (2009) Estimators of long-memory: Fourier versus wavelets. J Econom 151(2):159–177
Fratzscher M, Schneider D, Van Robays I (2013) Oil prices, exchange rates and asset prices. CESifo working paper no 4264
Gadanecz B, Jayaram K (2009) Measures of financial stability–a review. Bank for international settlements, IFC bulletin 3
Gallegati M, Gallegati M, Ramsey JB, Semmler W (2011) The us wage phillips curve across frequencies and over time. Oxf Bull Econ Stat 74(4):489–508
Gençay R, Selçuk F, Whitcher B (2002) An introduction to wavelets and other filtering methods in finance and economics. Academic, San Diego
Graham M, Kiviaho J, Nikkinen J (2013) Short-term and long-term dependencies of the s&p 500 index and commodity prices. Quant Finance 13(4):583–592
Hamilton J (2009) Causes and consequences of the oil shock of 2007-08. Brookings papers in economic activity 40(1):215–283
Hansen BE (1992) Tests for parameter instability in regressions with i (1) processes. J Bus Econ Stat 10:321–335
Hansen BE (1997) Approximate asymptotic p values for structuras-change tests. J Bus Econ Stat 15(1):60–67
Hansen P, Lunde A (2006) Realized variance and market microstructure noise. J Bus Econ Stat 24(2):127–161
Khordagui H, Al-Ajmi D (1993) Environmental impact of the gulf war: an integrated preliminary assessment. Environ Manage 17(4):557–562
Mallat S (1998) A wavelet tour of signal processing. Academic, San Diego
Markowitz H (1952) Portfolio selection. J Finance 7(1):77–91
Marshall JF (1994) The role of the investment horizon in optimal portfolio sequencing (an intuitive demonstration in discrete time). Financ Rev 29(4):557–576
McAleer M, Medeiros MC (2008) Realized volatility: a review. Econom Rev 27(1–3):10–45
Percival DB (1995) On estimationof the wavelet variance. Biometrika 82:619–631
Percival D, Walden A (2000) Wavelet methods for time series analysis. Cambridge University Press, Cambridge
Pindyck RS, Rotemberg JJ (1990) The excess co-movement of commodity prices. Econ J 100(403):1173–89
Ramsey JB (2002) Wavelets in economics and finance: past and future. Stud Nonlin Dyn Econom 6(3): Article 1, 1–27
Roueff F, Sachs R (2011) Locally stationary long memory estimation. Stoch Process Appl 121(4):813–844
Samuelson PA (1989) The judgment of economic science on rational portfolio management: indexing, timing, and long-horizon effects. J Portf Manage 16(1):4–12
Serroukh A, Walden AT, Percival DB (2000) Statistical properties and uses of the wavelet variance estimator for the scale analysis of time series. J Am Stat Assoc 95:184–196
Vacha L, Barunik J (2012) Co-movement of energy commodities revisited: evidence from wavelet coherence analysis. Energy Econ 34(1):241–247
Waldrop MM (1987) Computers amplify black monday: the sudden stock market decline raised questions about the role of computers; they may not have actually caused the crash, but may well have amplified it. Science 238(4827):602
Whitcher B, Guttorp P, Percival DB (1999) Mathematical background for wavelets estimators for cross covariance and cross correlation. Technical report 38, National Research Center for Statistics and the Environment
Whitcher B, Guttorp P, Percival DB (2000) Wavelet analysis of covariance with application to atmosferic time series. J Geophys Res 105:941–962
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-07061-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07060-5
Online ISBN: 978-3-319-07061-2
eBook Packages: Business and EconomicsEconomics and Finance (R0)