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Value-at-risk methodologies for effective energy portfolio risk management

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  • Halkos, George E.
  • Tsirivis, Apostolos S.

Abstract

Research has shown that the prediction of future variance through advanced GARCH type models is essential for an effective energy portfolio risk management. Still there has been a failure to provide a clear view on the specific amount of capital that is at risk on behalf of the investor or any party directly affected by the price fluctuations of specific or multiple energy commodities. Thus, it is necessary for risk managers to make one further step, determining the most robust and effective approach that will enable them to precisely monitor and accurately estimate the portfolio’s value-at-risk (VaR) which by definition, provides a good measure of the total actual amount at stake. Nevertheless, despite the variety of the variance models that have been developed and the range of various methodologies, most researchers have concluded that there is no model or specific methodology that outperforms all others. We find the best approach to minimize risk and accurately forecast the future potential losses is to adopt a methodology which takes into consideration the particular features which characterize the trade of energy products.

Suggested Citation

  • Halkos, George E. & Tsirivis, Apostolos S., 2019. "Value-at-risk methodologies for effective energy portfolio risk management," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 197-212.
  • Handle: RePEc:eee:ecanpo:v:62:y:2019:i:c:p:197-212
    DOI: 10.1016/j.eap.2019.03.002
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    2. Malik Zaka Ullah & Fouad Othman Mallawi & Mir Asma & Stanford Shateyi, 2022. "On the Conditional Value at Risk Based on the Laplace Distribution with Application in GARCH Model," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
    3. Rabin K. Jana & Aviral Kumar Tiwari & Shawkat Hammoudeh & Claudiu Albulescu, 2022. "Financial modeling, risk management of energy and environmental instruments and derivatives: past, present, and future," Annals of Operations Research, Springer, vol. 313(1), pages 1-7, June.
    4. George E. Halkos & Apostolos S. Tsirivis, 2019. "Energy Commodities: A Review of Optimal Hedging Strategies," Energies, MDPI, vol. 12(20), pages 1-19, October.

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    More about this item

    Keywords

    Energy commodities; Risk management; Value-at-risk (VaR);
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G3 - Financial Economics - - Corporate Finance and Governance
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • P28 - Economic Systems - - Socialist Systems and Transition Economies - - - Natural Resources; Environment
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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