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Cross-commodity analysis and applications to risk management

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Author Info

  • Börger, Reik
  • Cartea, Álvaro
  • Kiesel, Rüdiger
  • Schindlmayr, Gero

Abstract

The understanding of joint asset return distributions is an important ingredient for managing risks of portfolios. Although this is a well-discussed issue in fixed income and equity markets, it is a challenge for energy commodities. In this study we are concerned with describing the joint return distribution of energy-related commodities futures, namely power, oil, gas, coal, and carbon. The objective of the study is threefold. First, we conduct a careful analysis of empirical returns and show how the class of multivariate generalized hyperbolic distributions performs in this context. Second, we present how risk measures can be computed for commodity portfolios based on generalized hyperbolic assumptions. And finally,we discuss the implications of our findings for risk management analyzing the exposure of power plants, which represent typical energy portfolios. Our main findings are that risk estimates based on a normal distribution in the context of energy commodities can be statistically improved using generalized hyperbolic distributions. Those distributions are flexible enough to incorporate many characteristics of commodity returns and yield more accurate risk estimates. Our analysis of the market suggests that carbon allowances can be a helpful tool for controlling the risk exposure of a typical energy portfolio representing a power plant

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File URL: http://e-archivo.uc3m.es/bitstream/10016/12155/1/cross_cartea_JFM_2009_ps.pdf
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Bibliographic Info

Paper provided by Universidad Carlos III de Madrid in its series Open Access publications from Universidad Carlos III de Madrid with number info:hdl:10016/12155.

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Length: 219 p.
Date of creation: Mar 2009
Date of revision:
Publication status: Published in The Journal of Futures Markets (2009-03) v.v. 29, p.197-217
Handle: RePEc:ner:carlos:info:hdl:10016/12155

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Web page: http://www.uc3m.es

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Keywords: Commodities; Risk;

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  1. Cartea, Álvaro & Figueroa, Marcelo G., 2005. "Pricing in electricity markets : a mean reverting jump diffusion model with seasonality," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/12199, Universidad Carlos III de Madrid.
  2. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
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