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Modeling dynamic conditional correlations in WTI oil forward and futures returns

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  • Lanza, Alessandro
  • Manera, Matteo
  • McAleer, Michael

Abstract

This paper estimates the dynamic conditional correlations in the returns on WTI oil one-month forward prices, and one-, three-, six-, and twelve-month futures prices, using recently developed multivariate conditional volatility models. The dynamic correlations enable a determination of whether the forward and various futures returns are substitutes or complements, which are crucial for deciding whether or not to hedge against unforeseen circumstances. The models are estimated using daily data on WTI oil forward and futures prices, and their associated returns, from 3 January 1985 to 16 January 2004. At the univariate level, the estimates are statistically significant, with the occasional asymmetric effect in which negative shocks have a greater impact on volatility than positive shocks. In all cases, both the short- and long-run persistence of shocks are statistically significant. Among the five returns, there are ten conditional correlations, with the highest estimate of constant conditional correlation being 0.975 between the volatilities of the three-month and six-month futures returns, and the lowest being 0.656 between the volatilities of the forward and twelve-month futures returns. The dynamic conditional correlations can vary dramatically, being negative in four of ten cases and being close to zero in another five cases. Only in the case of the dynamic volatilities of the three-month and six-month futures returns is the range of variation relatively narrow, namely (0.832, 0.996). Thus, in general, the dynamic volatilities in the returns in the WTI oil forward and future prices can be either independent or interdependent over time.
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  • Lanza, Alessandro & Manera, Matteo & McAleer, Michael, 2006. "Modeling dynamic conditional correlations in WTI oil forward and futures returns," Finance Research Letters, Elsevier, vol. 3(2), pages 114-132, June.
  • Handle: RePEc:eee:finlet:v:3:y:2006:i:2:p:114-132
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    Cited by:

    1. Matteo Manera & Marcella Nicolini & Ilaria Vignati, 2012. "Returns in commodities futures markets and financial speculation: a multivariate GARCH approach," Quaderni di Dipartimento 170, University of Pavia, Department of Economics and Quantitative Methods.
    2. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.
    3. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    4. Massimiliano Caporin & Michael McAleer, 2013. "Ten Things You Should Know about the Dynamic Conditional Correlation Representation," Econometrics, MDPI, Open Access Journal, vol. 1(1), pages 1-12, June.
    5. Rizvi , Syed Aun R & Arshad , Shaista, 2014. "An Empirical Study of Islamic Equity as a Better Alternative during Crisis Using Multivariate GARCH DCC," Islamic Economic Studies, The Islamic Research and Training Institute (IRTI), vol. 22, pages 159-184.
    6. George P. Papaioannou & Christos Dikaiakos & George Evangelidis & Panagiotis G. Papaioannou & Dionysios S. Georgiadis, 2015. "Co-Movement Analysis of Italian and Greek Electricity Market Wholesale Prices by Using a Wavelet Approach," Energies, MDPI, Open Access Journal, vol. 8(10), pages 1-30, October.
    7. Massimiliano Caporin & Michael McAleer, 2013. "Ten Things you should know about DCC," Tinbergen Institute Discussion Papers 13-048/III, Tinbergen Institute.
    8. Rahim, Adam Mohamed & Masih, Mansur, 2014. "Portfolio Diversification Benefits of Islamic Stocks and Malaysia’s Major Trading Partners:MGARCH-DCC and Wavelet Correlation Approaches," MPRA Paper 58903, University Library of Munich, Germany.
    9. Lien, Donald & Yang, Li, 2008. "Asymmetric effect of basis on dynamic futures hedging: Empirical evidence from commodity markets," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 187-198, February.
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    11. Aboura, Sofiane & Chevallier, Julien, 2013. "Leverage vs. feedback: Which Effect drives the oil market?," Finance Research Letters, Elsevier, vol. 10(3), pages 131-141.
    12. Rahim, Adam Mohamed & Masih, Mansur, 2014. "Effects of Political Turmoil (Arab Spring) on Portfolio Diversification Benefits: Perspectives of the Moroccan Islamic Stock investors," MPRA Paper 58832, University Library of Munich, Germany.
    13. Vargas, Gregorio A., 2008. "What Drives the Dynamic Conditional Correlation of Foreign Exchange and Equity Returns?," MPRA Paper 7174, University Library of Munich, Germany.
    14. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, Open Access Journal, vol. 5(4), pages 1-26, April.
    15. Buriev, Abdul Aziz & Masih, Mansur, 2015. "Impact of Arab uprising on Portfolio diversification benefits at different investment horizons for the Turkish investors in relation to the regional stock markets: Multivariate GARCH-DCC and Wavelet c," MPRA Paper 65233, University Library of Munich, Germany.
    16. He, Kaijian & Wang, Lijun & Zou, Yingchao & Lai, Kin Keung, 2014. "Value at risk estimation with entropy-based wavelet analysis in exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 62-71.
    17. Rizvi, Syed Aun & Masih, Mansur, 2013. "Do Shariah (Islamic) Indices Provide a Safer Avenue in Crisis? Empirical Evidence from Dow Jones Indices using Multivariate GARCH-DCC," MPRA Paper 57701, University Library of Munich, Germany.
    18. Matteo Manera, Marcella Nicolini, and Ilaria Vignati, 2013. "Financial Speculation in Energy and Agriculture Futures Markets: A Multivariate GARCH Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
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    22. Rahim, Adam Mohamed & Masih, Mansur, 2016. "Portfolio diversification benefits of Islamic investors with their major trading partners: Evidence from Malaysia based on MGARCH-DCC and wavelet approaches," Economic Modelling, Elsevier, vol. 54(C), pages 425-438.
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    24. repec:gam:jsusta:v:9:y:2017:i:6:p:1071-:d:101981 is not listed on IDEAS
    25. Kundu, Srikanta & Sarkar, Nityananda, 2016. "Return and volatility interdependences in up and down markets across developed and emerging countries," Research in International Business and Finance, Elsevier, vol. 36(C), pages 297-311.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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