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Value-at-risk estimation with the optimal dynamic biofuel portfolio

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  • Chang, Ting-Huan
  • Su, Hsin-Mei
  • Chiu, Chien-Liang

Abstract

In the past, petroleum companies only paid attention to hedging the variation in the crude oil price and volatility. However, they have now expanded their analysis to encompass renewable sources, such as corn and soybeans, under the current low-carbon biofuel obligations. This paper employs GARCH(1,1) and ARJI models to estimate the one-day-ahead Value-at-Risk (VaR) of the optimal dynamic biofuel portfolio, which consists of crude oil, corn and soybeans. The optimal blended standard is subject to the dual limitations of minimum production costs and the lowest biofuel using requirements. Our empirical findings confirm that the ARJI model is more suitable than the GARCH (1,1) model and further captures the discontinuous jump behavior from the in-the-sample data. The results of out-of-sample forecasts also are represented that our models play important roles in VaR estimation and risk management for biofuel portfolio. We therefore suggest that the petroleum companies should simultaneously pay attention to jump risk in hedging material costs in the prices of energy-related crops.

Suggested Citation

  • Chang, Ting-Huan & Su, Hsin-Mei & Chiu, Chien-Liang, 2011. "Value-at-risk estimation with the optimal dynamic biofuel portfolio," Energy Economics, Elsevier, vol. 33(2), pages 264-272, March.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:2:p:264-272
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    Cited by:

    1. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Causality and predictability in distribution: The ethanol–food price relation revisited," Energy Economics, Elsevier, vol. 42(C), pages 152-160.
    2. Chunyang Zhou & Xiao Qin & Xundi Diao & Yingchen He, 2016. "Estimating multi-period Value at Risk of oil futures prices," Applied Economics, Taylor & Francis Journals, vol. 48(32), pages 2994-3004, July.
    3. Zhou, Chunyang & Wu, Chongfeng & Wang, Yudong, 2019. "Dynamic portfolio allocation with time-varying jump risk," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 113-124.
    4. Kristoufek, Ladislav & Janda, Karel & Zilberman, David, 2012. "Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective," Energy Economics, Elsevier, vol. 34(5), pages 1380-1391.
    5. Chunyang Zhou & Chongfeng Wu & Weidong Xu, 2020. "Incorporating time‐varying jump intensities in the mean‐variance portfolio decisions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(3), pages 460-478, March.
    6. Serra, Teresa & Zilberman, David, 2013. "Biofuel-related price transmission literature: A review," Energy Economics, Elsevier, vol. 37(C), pages 141-151.
    7. Kristoufek, Ladislav & Janda, Karel & Zilberman, David, 2012. "Relationship Between Prices of Food, Fuel and Biofuel," 131st Seminar, September 18-19, 2012, Prague, Czech Republic 135793, European Association of Agricultural Economists.
    8. Serra, Teresa, 2012. "Biofuel-related price volatility literature: a review and new approaches," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126057, International Association of Agricultural Economists.
    9. Yen-Hsien Lee & Ya-Ling Huang & Chun-Yu Wu, 2013. "Conditional Jump Dynamics in the Stock Prices of Alternative Energy Companies," International Journal of Energy Economics and Policy, Econjournals, vol. 3(3), pages 288-296.

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