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Modeling conditional correlations for risk diversification in crude oil markets

Author

Listed:
  • Chia-Lin Chang
  • Michael McAleer
  • Roengchai Tansuchat

Abstract

ABSTRACT This paper estimates univariate and multivariate conditional volatility and conditional correlation models of spot, forward and futures returns from three major benchmarks of the international crude oil markets, namely Brent,West Texas Intermediate and Dubai, to aid with the process of risk diversification. Conditional correlations are estimated using Bollerslev's constant conditional correlation model, Ling and McAleer's vector autoregressive moving average-generalized autoregressive conditional heteroscedasticity (VARMA-GARCH) model, the vector autoregressive moving average-asymmetric generalized autoregressive conditional heteroscedasticity (VARMA-AGARCH) model of McAleer et al and a dynamic conditional correlation model by Engle. The paper also presents the autoregressive conditional heteroscedasticity and generalized autoregressive conditional heteroscedasticity effects for returns and shows the presence of significant interdependencies in the conditional volatilities across returns for each market.

Suggested Citation

  • Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, . "Modeling conditional correlations for risk diversification in crude oil markets," Journal of Energy Markets, Journal of Energy Markets.
    • Handle: RePEc:rsk:journ2:2160797
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    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. 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.
    6. 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.
    7. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    8. 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.
    9. Manera, Matteo & Nicolini, Marcella & Vignati, Ilaria, 2012. "Returns in Commodities Futures Markets and Financial Speculation: A Multivariate GARCH Approach," Energy: Resources and Markets 122868, Fondazione Eni Enrico Mattei (FEEM).
    10. 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.
    11. 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.
    12. Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2010. "Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets," KIER Working Papers 717, Kyoto University, Institute of Economic Research.
    13. Halkos, George & Tsirivis, Apostolos, 2019. "Using Value-at-Risk for effective energy portfolio risk management," MPRA Paper 91674, University Library of Munich, Germany.
    14. Georgios Charalampous & Reinhard Madlener, 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).
    15. Matteo Manera & Marcella Nicolini & Ilaria Vignati, 2013. "Financial Speculation in Energy and Agriculture Futures Markets: A Multivariate GARCH Approach," The Energy Journal, , vol. 34(3), pages 55-82, July.
    16. 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.

    More about this item

    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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