IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/47134.html
   My bibliography  Save this paper

Hedging China’s Energy Oil Market Risks

Author

Listed:
  • Su, Yongyang
  • Lau, Chi Keung Marco
  • Tan, Na

Abstract

This paper is the first study to examine the effectiveness of the Shanghai Fuel Oil Futures Contract (SHF) in risk reduction on the Chinese energy oil market. We find that the SHF contract can help investors reduce risk by approximately 45%, lower than empirical evidence in developed markets, when weekly data are applied. In contrast, when using daily data SHF contract can only help reduce risk by approximately 9%. The Tokyo Oil Futures Contract (TKF), however, performs two times better, reducing risk by around 17%. The empirical results are robust when variance complicated bivariate GARCH (BGARCH) and bivariate distributions are used. Our results imply the energy oil futures market in China is not well-established and further policy is needed to improve market efficiency.

Suggested Citation

  • Su, Yongyang & Lau, Chi Keung Marco & Tan, Na, 2013. "Hedging China’s Energy Oil Market Risks," MPRA Paper 47134, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:47134
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/47134/1/MPRA_paper_47134.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Donald Lien & Y. K. Tse & Albert Tsui, 2002. "Evaluating the hedging performance of the constant-correlation GARCH model," Applied Financial Economics, Taylor & Francis Journals, vol. 12(11), pages 791-798.
    2. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    3. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-124, April-Jun.
    4. Sung Yong Park & Sang Young Jei, 2010. "Estimation and hedging effectiveness of time‐varying hedge ratio: Flexible bivariate garch approaches," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(1), pages 71-99, January.
    5. Lien, Donald, 2009. "A note on the hedging effectiveness of GARCH models," International Review of Economics & Finance, Elsevier, vol. 18(1), pages 110-112, January.
    6. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    7. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    8. Chan, Kalok & Chan, K C & Karolyi, G Andrew, 1991. "Intraday Volatility in the Stock Index and Stock Index Futures Markets," The Review of Financial Studies, Society for Financial Studies, vol. 4(4), pages 657-684.
    9. Atreya Chakraborty & John Barkoulas, 1999. "Dynamic futures hedging in currency markets," The European Journal of Finance, Taylor & Francis Journals, vol. 5(4), pages 299-314.
    10. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martínez Ceseña, Eduardo A. & Good, Nicholas & Mancarella, Pierluigi, 2015. "Electrical network capacity support from demand side response: Techno-economic assessment of potential business cases for small commercial and residential end-users," Energy Policy, Elsevier, vol. 82(C), pages 222-232.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Lau & Yongyang Su & Na Tan & Zhe Zhang, 2014. "Hedging China’s energy oil market risks," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(1), pages 99-112, June.
    2. Kotkatvuori-Örnberg, Juha, 2016. "Dynamic conditional copula correlation and optimal hedge ratios with currency futures," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 60-69.
    3. Hou, Yang & Li, Steven, 2013. "Hedging performance of Chinese stock index futures: An empirical analysis using wavelet analysis and flexible bivariate GARCH approaches," Pacific-Basin Finance Journal, Elsevier, vol. 24(C), pages 109-131.
    4. Chang, Chia-Lin & González-Serrano, Lydia & Jimenez-Martin, Juan-Angel, 2013. "Currency hedging strategies using dynamic multivariate GARCH," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 164-182.
    5. Sharma, Udayan & Karmakar, Madhusudan, 2023. "Measuring minimum variance hedging effectiveness: Traditional vs. sophisticated models," International Review of Financial Analysis, Elsevier, vol. 87(C).
    6. Ubukata, Masato, 2018. "Dynamic hedging performance and downside risk: Evidence from Nikkei index futures," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 270-281.
    7. Hou, Yang & Li, Steven & Wen, Fenghua, 2019. "Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach," Energy Economics, Elsevier, vol. 83(C), pages 119-143.
    8. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2022. "The influence of the COVID-19 pandemic on the hedging functionality of Chinese financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    9. Yu-Sheng Lai, 2018. "Dynamic hedging with futures: a copula-based GARCH model with high-frequency data," Review of Derivatives Research, Springer, vol. 21(3), pages 307-329, October.
    10. Yu‐Sheng Lai, 2022. "Use of high‐frequency data to evaluate the performance of dynamic hedging strategies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 104-124, January.
    11. Bessler, Wolfgang & Leonhardt, Alexander & Wolff, Dominik, 2016. "Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 239-256.
    12. Sofiane Aboura & Julien Chevallier, 2014. "Cross‐market spillovers with ‘volatility surprise’," Review of Financial Economics, John Wiley & Sons, vol. 23(4), pages 194-207, November.
    13. Yang (Greg) Hou & Mark Holmes, 2020. "Do higher order moments of return distribution provide better decisions in minimum-variance hedging? Evidence from US stock index futures," Australian Journal of Management, Australian School of Business, vol. 45(2), pages 240-265, May.
    14. Hou, Yang (Greg) & Li, Steven, 2020. "Volatility and skewness spillover between stock index and stock index futures markets during a crash period: New evidence from China," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 166-188.
    15. repec:ipg:wpaper:2014-469 is not listed on IDEAS
    16. Kim, Myeong Jun & Park, Sung Y., 2016. "Optimal conditional hedge ratio: A simple shrinkage estimation approach," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 139-156.
    17. You‐How Go & Jia‐Jun Teo & Kam Fong Chan, 2023. "The effectiveness of crude oil futures hedging during infectious disease outbreaks in the 21st century," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1559-1575, November.
    18. Kim, Jong-Min & Jung, Hojin, 2016. "Linear time-varying regression with Copula–DCC–GARCH models for volatility," Economics Letters, Elsevier, vol. 145(C), pages 262-265.
    19. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    20. Sofiane Aboura & Julien Chevallier, 2014. "Cross-Market Spillovers with ‘Volatility Surprise’," Working Papers hal-04141310, HAL.
    21. Yudong Wang & Chongfeng Wu & Li Yang, 2015. "Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy?," Management Science, INFORMS, vol. 61(12), pages 2870-2889, December.

    More about this item

    Keywords

    China Energy Oil Market; Hedging Risk Performance; Bivariate GARCH model.;
    All these keywords.

    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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:47134. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.