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Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets

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  • Chia-Lin Chang

    (Department of Applied Economics, National Chung Hsing University)

  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

  • Roengchai Tansuchat

    (Faculty of Economics, Maejo University)

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.

Suggested Citation

  • 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.
  • Handle: RePEc:kyo:wpaper:717
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    References listed on IDEAS

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    Cited by:

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    2. Restrepo, Natalia & Uribe, Jorge M. & Manotas, Diego, 2018. "Financial risk network architecture of energy firms," Applied Energy, Elsevier, vol. 215(C), pages 630-642.
    3. Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2020. "Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets," Energy Economics, Elsevier, vol. 92(C).
    4. Lin, Xiaoqiang & Chen, Qiang & Tang, Zhenpeng, 2014. "Dynamic hedging strategy in incomplete market: Evidence from Shanghai fuel oil futures market," Economic Modelling, Elsevier, vol. 40(C), pages 81-90.
    5. Jozef Baruník and Ev~en Kocenda, 2019. "Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    6. Chun, Dohyun & Cho, Hoon & Kim, Jihun, 2019. "Crude oil price shocks and hedging performance: A comparison of volatility models," Energy Economics, Elsevier, vol. 81(C), pages 1132-1147.
    7. Syed jawad hussain Shahzad & Saba Ameer & Muhammad Shahbaz, 2016. "Disaggregating the correlation under bearish and bullish markets: A Quantile-quantile approach," Economics Bulletin, AccessEcon, vol. 36(4), pages 2465-2473.
    8. Kumar, Satish & Pradhan, Ashis Kumar & Tiwari, Aviral Kumar & Kang, Sang Hoon, 2019. "Correlations and volatility spillovers between oil, natural gas, and stock prices in India," Resources Policy, Elsevier, vol. 62(C), pages 282-291.
    9. 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.
    10. Zhang, Dayong & Ji, Qiang & Kutan, Ali M., 2019. "Dynamic transmission mechanisms in global crude oil prices: Estimation and implications," Energy, Elsevier, vol. 175(C), pages 1181-1193.
    11. Zhou, Xinmiao & Zhang, Junru & Zhang, Zhaoyong, 2021. "How does news flow affect cross-market volatility spillovers? Evidence from China’s stock index futures and spot markets," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 196-213.
    12. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2018. "Public information arrival, price discovery and dynamic correlations in the Chinese renminbi markets," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 168-186.
    13. Chkili, Walid & Aloui, Chaker & Nguyen, Duc Khuong, 2014. "Instabilities in the relationships and hedging strategies between crude oil and US stock markets: Do long memory and asymmetry matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 354-366.
    14. An, Sufang & Gao, Xiangyun & An, Haizhong & An, Feng & Sun, Qingru & Liu, Siyao, 2020. "Windowed volatility spillover effects among crude oil prices," Energy, Elsevier, vol. 200(C).
    15. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
    16. Kunlapath Sukcharoen & Hankyeung Choi & David J. Leatham, 2015. "Optimal gasoline hedging strategies using futures contracts and exchange-traded funds," Applied Economics, Taylor & Francis Journals, vol. 47(32), pages 3482-3498, July.
    17. Bentes, Sonia R., 2018. "Is stock market volatility asymmetric? A multi-period analysis for five countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 258-265.
    18. Meng, Fanyi & Liu, Li, 2019. "Analyzing the economic sources of oil price volatility: An out-of-sample perspective," Energy, Elsevier, vol. 177(C), pages 476-486.
    19. Pan, Zhiyuan & Wang, Yudong & Yang, Li, 2014. "Hedging crude oil using refined product: A regime switching asymmetric DCC approach," Energy Economics, Elsevier, vol. 46(C), pages 472-484.
    20. Yen-Hsien Lee & Ya-Ling Huang & Chun-Yu Wu, 2014. "Dynamic Correlations and Volatility Spillovers between Crude Oil and Stock Index Returns: The Implications for Optimal Portfolio Construction," International Journal of Energy Economics and Policy, Econjournals, vol. 4(3), pages 327-336.
    21. Zhang, Bing & Wang, Peijie, 2014. "Return and volatility spillovers between china and world oil markets," Economic Modelling, Elsevier, vol. 42(C), pages 413-420.
    22. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2019. "Decoding the Australian electricity market: New evidence from three-regime hidden semi-Markov model," Energy Economics, Elsevier, vol. 78(C), pages 129-142.
    23. Walid Chkili, 2015. "Gold–oil prices co-movements and portfolio diversification implications," Economics Bulletin, AccessEcon, vol. 35(4), pages 2832-2845.

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    More about this item

    Keywords

    Volatility spillovers; multivariate GARCH; conditional correlation; crude oil prices; spot returns; forward returns; futures returns;
    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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