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Estimation and hedging effectiveness of time‐varying hedge ratio: Flexible bivariate garch approaches

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  • Sung Yong Park
  • Sang Young Jei

Abstract

Bollerslev's ( 1990 , Review of Economics and Statistics, 52, 5–59) constant conditional correlation and Engle's (2002, Journal of Business & Economic Statistics, 20, 339–350) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models are usually used to estimate time‐varying hedge ratios. In this study, we extend the above model to more flexible ones to analyze the behavior of the optimal conditional hedge ratio based on two (BGARCH) models: (i) adopting more flexible bivariate density functions such as a bivariate skewed‐t density function; (ii) considering asymmetric individual conditional variance equations; and (iii) incorporating asymmetry in the conditional correlation equation for the DCC‐based model. Hedging performance in terms of variance reduction and also value at risk and expected shortfall of the hedged portfolio are also conducted. Using daily data of the spot and futures returns of corn and soybeans we find asymmetric and flexible density specifications help increase the goodness‐of‐fit of the estimated models, but do not guarantee higher hedging performance. We also find that there is an inverse relationship between the variance of hedge ratios and hedging effectiveness. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:71–99, 2010

Suggested Citation

  • 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.
  • Handle: RePEc:wly:jfutmk:v:30:y:2010:i:1:p:71-99
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    Cited by:

    1. 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.
    2. repec:kap:compec:v:52:y:2018:i:1:d:10.1007_s10614-017-9662-z is not listed on IDEAS
    3. Mun, Kyung-Chun, 2016. "Hedging bank market risk with futures and forwards," The Quarterly Review of Economics and Finance, Elsevier, vol. 61(C), pages 112-125.
    4. Aragó, Vicent & Salvador, Enrique, 2011. "Sudden changes in variance and time varying hedge ratios," European Journal of Operational Research, Elsevier, vol. 215(2), pages 393-403, December.
    5. Karali, Berna, 2012. "Do USDA Announcements Affect Comovements Across Commodity Futures Returns?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 0(Number 1), pages 1-21, April.
    6. repec:eee:finlet:v:22:y:2017:i:c:p:158-162 is not listed on IDEAS
    7. Hou, Yang & Holmes, Mark, 2017. "On the effects of static and autoregressive conditional higher order moments on dynamic optimal hedging," MPRA Paper 82000, University Library of Munich, Germany.
    8. 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.
    9. Su, Yongyang & Lau, Chi Keung Marco & Tan, Na, 2013. "Hedging China’s Energy Oil Market Risks," MPRA Paper 47134, University Library of Munich, Germany.
    10. Hou, Yang & Nartea, Gilbert, 2017. "Price Discovery in the Stock Index Futures Market: Evidence from the Chinese stock market crash," MPRA Paper 81995, University Library of Munich, Germany.
    11. Ubukata, Masato & Watanabe, Toshiaki, 2015. "Evaluating the performance of futures hedging using multivariate realized volatility," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 148-171.
    12. 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.
    13. Hou, Yang & Li, Steven, 2017. "Time-Varying Price Discovery and Autoregressive Loading Factors: Evidence from S&P 500 Cash and E-Mini Futures Markets," MPRA Paper 81999, University Library of Munich, Germany.
    14. Jing-Yi Lai, 2012. "An empirical study of the impact of skewness and kurtosis on hedging decisions," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1827-1837, December.
    15. 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.
    16. 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.

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