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Volatility linkages between energy and agricultural commodity prices

Author Info

Listed author(s):
  • Brenda López Cabrera,
  • Franziska Schulz,

Abstract

In this paper we investigate price and volatility risk originating in link- ages between energy and agricultural commodity prices in Germany and study their dynamics over time. We propose an econometric approach to quantify the volatility and correlation risk structure, which has a large impact for in- vestment and hedging strategies of market participants as well as for policy makers. Volatilities and their short and long run linkages (spillovers) are an- alyzed using a dynamic conditional correlation GARCH model as well as a multivariate multiplicative volatility model. Our approach provides a exible and accurate fitting procedure for volatility and correlation risk.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2013-042.pdf
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2013-042.

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Length: 23 pages
Date of creation: Sep 2013
Handle: RePEc:hum:wpaper:sfb649dp2013-042
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Keywords: Energy; Agriculture; Biodiesel; Commodities; Interdependencies; Volatility Spillovers;

Other versions of this item:

Find related papers by JEL classification:
  • G19 - Financial Economics - - General Financial Markets - - - Other
  • G29 - Financial Economics - - Financial Institutions and Services - - - Other
  • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
  • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
  • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other
  • Q59 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Other

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Cited by:

  1. Poeschel, Friedrich, 2012. "Assortative matching through signals," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62061, Verein für Socialpolitik / German Economic Association.
  2. Rangga Handika & Rangga Handika & Sigit Triandaru, 2016. "Is the Best Generalized Autoregressive Conditional Heteroskedasticity(p,q) Value-at-risk Estimate also the Best in Reality? An Evidence from Australian Interconnected Power Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 6(4), pages 814-821.
  3. Chia-Lin Chang & Michael McAleer & Yu-Ann Wang, 2016. "Modelling Volatility Spillovers for Bio-ethanol, Sugarcane and Corn Spot and Futures Prices," Tinbergen Institute Discussion Papers 16-014/III, Tinbergen Institute, revised 30 Jan 2017.
  4. Chang, C-L. & McAleer, M.J. & Wang, Y-A., 2016. "Modelling Volatility Spillovers for Bio-ethanol, Sugarcane and Corn," Econometric Institute Research Papers EI2016-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  5. Siami-Namini, Sima & Hudson, Darren, 2017. "Volatility Spillover Between Oil Prices, Us Dollar Exchange Rates And International Agricultural Commodities Prices," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252845, Southern Agricultural Economics Association.
  6. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.

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