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Analyzing and Forecasting Volatility Spillovers, Asymmetries and Hedging in Major Oil Markets

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  • Chia-Lin Chang
  • Michael McAleer

    ()
    (University of Canterbury)

  • Roengchai Tansuchat

Abstract

Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets.

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File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1019.pdf
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Bibliographic Info

Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 10/19.

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Length: 28 pages
Date of creation: 01 Apr 2010
Date of revision:
Handle: RePEc:cbt:econwp:10/19

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Keywords: Volatility spillovers; multivariate GARCH; conditional correlation; asymmetries; hedging;

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References

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Cited by:
  1. Rafik Nazarian & Esmaeil Naderi & Nadiya G. Alikhani & Ashkan Amiri, 2014. "Long Memory Analysis: An Empirical Investigation," International Journal of Economics and Financial Issues, Econjournals, vol. 4(1), pages 16-26.
  2. 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.
  3. Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," KIER Working Papers 743, Kyoto University, Institute of Economic Research.
  4. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
  5. Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Working Papers 2014-325, Department of Research, Ipag Business School.
  6. 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.
  7. 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 1018-1043, April.
  8. Jozef Barunik & Evzen Kocenda & Lukas Vacha, 2014. "How does bad and good volatility spill over across petroleum markets?," Papers 1405.2445, arXiv.org.
  9. 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.
  10. Conrad, Christian & Weber, Enzo, 2013. "Measuring Persistence in Volatility Spillovers," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79850, Verein für Socialpolitik / German Economic Association.
  11. Jäschke, Stefan, 2014. "Estimation of risk measures in energy portfolios using modern copula techniques," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 359-376.
  12. Gronwald, Marc, 2012. "A characterization of oil price behavior — Evidence from jump models," Energy Economics, Elsevier, vol. 34(5), pages 1310-1317.
  13. Lee, Chien-Chiang & Zeng, Jhih-Hong, 2011. "The impact of oil price shocks on stock market activities: Asymmetric effect with quantile regression," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1910-1920.
  14. Lu, Feng-bin & Hong, Yong-miao & Wang, Shou-yang & Lai, Kin-keung & Liu, John, 2014. "Time-varying Granger causality tests for applications in global crude oil markets," Energy Economics, Elsevier, vol. 42(C), pages 289-298.

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