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Modelling conditional correlations for risk diversification in crude oil markets

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  • Chang, C-L.
  • McAleer, M.J.
  • Tansuchat, R.

Abstract

This paper estimates univariate and multivariate conditional volatility and conditional correlation models of spot, forward and futures returns from three major benchmarks of international crude oil markets, namely Brent, WTI and Dubai, to aid in risk diversification. Conditional correlations are estimated using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer et al. (2009), and DCC model of Engle (2002). The paper also presents the ARCH and GARCH effects for returns and shows the presence of significant interdependences in the conditional volatilities across returns for each market. The estimates of volatility spillovers and asymmetric effects for negative and positive shocks on conditional variance suggest that VARMA-GARCH is superior to the VARMA-AGARCH model. In addition, the DCC model gives statistically significant estimates for the returns in each market, which shows that constant conditional correlations do not hold in practice.

Suggested Citation

  • Chang, C-L. & McAleer, M.J. & Tansuchat, R., 2009. "Modelling conditional correlations for risk diversification in crude oil markets," Econometric Institute Research Papers EI 2009-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:16105
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    Cited by:

    1. Chang, C-L. & McAleer, M.J. & Tansuchat, R., 2010. "Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets," Econometric Institute Research Papers EI 2010-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. 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.
    3. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2010. "Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets," Energy Economics, Elsevier, vol. 32(6), pages 1445-1455, November.
    4. Jin, Xiaoye & Xiaowen Lin, Sharon & Tamvakis, Michael, 2012. "Volatility transmission and volatility impulse response functions in crude oil markets," Energy Economics, Elsevier, vol. 34(6), pages 2125-2134.
    5. 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.
    6. Halkos, George & Tsirivis, Apostolos, 2019. "Using Value-at-Risk for effective energy portfolio risk management," MPRA Paper 91674, University Library of Munich, Germany.
    7. Chia-Lin Chang & Michael McAleer & Jiarong Tian, 2019. "Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China," Energies, MDPI, vol. 12(8), pages 1-24, April.
    8. 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.
    9. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    10. Ching-Chun Wei, 2016. "Modeling and Analyzing the Mean and Volatility Relationship between Electricity Price Returns and Fuel Market Returns," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(7), pages 1-55, July.
    11. Chia-Lin Chang & Chia-Ping Liu & Michael McAleer, 2016. "Volatility Spillovers for Spot, Futures, and ETF Prices in Energy and Agriculture," Tinbergen Institute Discussion Papers 16-046/III, Tinbergen Institute.
    12. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    13. Charalampous, Georgios & Madlener, Reinhard, 2013. "Risk Management and Portfolio Optimization for Gas- and Coal-fired Power Plants in Germany: A Multivariate GARCH Approach," FCN Working Papers 23/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    14. 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).
    15. Lu, Jin-Ray & Lee, Pei-Hsuan & Chuang, I-Yuan, 2011. "Estimation of oil firm's systematic risk via composite time-varying models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(11), pages 2389-2399.
    16. Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.

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    More about this item

    Keywords

    conditional correlations; crude oil spot prices; forward prices; futures prices; risk diversification;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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