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Time-frequency analysis of crude oil and S&P500 futures contracts

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  • Joseph McCarthy
  • Alexei G. Orlov

Abstract

We use frequency-domain techniques, namely wavelets and cross-spectra, to examine the association between the daily prices of crude oil futures and daily S&P500 futures closing prices over the past several decades. We investigate contemporaneous and lag--lead relationships in levels and returns. It is our belief that the wavelet and cross-spectral analyses employed in this paper offer insights regarding the relationship between oil prices and stock returns that are not apparent from a conventional time-domain framework. Our findings cast doubt on the purported negative relationship between oil and the U.S. stock market. Our analysis suggests that oil prices lead oil volume, and S&P500 trading volume leads S&P500 prices.

Suggested Citation

  • Joseph McCarthy & Alexei G. Orlov, 2012. "Time-frequency analysis of crude oil and S&P500 futures contracts," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1893-1908, December.
    • Handle: RePEc:taf:quantf:v:12:y:2012:i:12:p:1893-1908
    DOI: 10.1080/14697688.2012.686669
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    References listed on IDEAS

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    1. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    2. Ke Tang & Wei Xiong, 2010. "Index Investment and Financialization of Commodities," NBER Working Papers 16385, National Bureau of Economic Research, Inc.
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    Cited by:

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    2. Jiang, Chun & Chang, Tsangyao & Li, Xiao-Lin, 2015. "Money growth and inflation in China: New evidence from a wavelet analysis," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 249-261.
    3. Storhas, Dominik P. & De Mello, Lurion & Singh, Abhay Kumar, 2020. "Multiscale lead-lag relationships in oil and refined product return dynamics: A symbolic wavelet transfer entropy approach," Energy Economics, Elsevier, vol. 92(C).
    4. Allaj, Erindi & Sanfelici, Simona, 2023. "Early Warning Systems for identifying financial instability," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1777-1803.
    5. Bahmani-Oskooee, Mohsen & Chang, Tsangyao & Ranjbar, Omid, 2016. "Asymmetric causality using frequency domain and time-frequency domain (wavelet) approaches," Economic Modelling, Elsevier, vol. 56(C), pages 66-78.
    6. Yipeng Yang & Allanus Tsoi, 2016. "A Level Set Analysis and A Nonparametric Regression on S&P 500 Daily Return," IJFS, MDPI, vol. 4(1), pages 1-24, February.
    7. Ehsan Bagheri & Seyed Babak Ebrahimi & Arman Mohammadi & Mahsa Miri & Stelios Bekiros, 2022. "The Dynamic Volatility Connectedness Structure of Energy Futures and Global Financial Markets: Evidence From a Novel Time–Frequency Domain Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1087-1111, March.
    8. Martín-Barragán, Belén & Ramos, Sofia B. & Veiga, Helena, 2015. "Correlations between oil and stock markets: A wavelet-based approach," Economic Modelling, Elsevier, vol. 50(C), pages 212-227.
    9. Venkata Sai Srinivasa Rao Muramalla & Hassan Ali Alqahtani, 2020. "Long Run Association of Oil Prices and Stock Prices: A Case of Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 593-600.
    10. Li, Xiao-Lin & Chang, Tsangyao & Miller, Stephen M. & Balcilar, Mehmet & Gupta, Rangan, 2015. "The co-movement and causality between the U.S. housing and stock markets in the time and frequency domains," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 220-233.
    11. Ladislav Kristoufek & Karel Janda & David Zilberman, 2015. "Co-movements of Ethanol Related Prices: Evidence from Brazil and the USA," CAMA Working Papers 2015-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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