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Measuring risk of crude oil at extreme quantiles

Author

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  • Sasa Zikovic

    () (University of Rijeka, Faculty of Economics, Rijeka, Croatia)

Abstract

The purpose of this paper is to investigate the performance of VaR models at measuring risk for WTI oil one-month futures returns. Risk models, ranging from industry standards such as RiskMetrics and historical simulation to conditional extreme value model, are used to calculate commodity market risk at extreme quantiles: 0.95, 0.99, 0.995 and 0.999 for both long and short trading positions. Our results show that out of the tested fat tailed distributions, generalised Pareto distribution provides the best fit to both tails of oil returns although tails differ significantly, with the right tail having a higher tail index, indicative of more extreme events. The main conclusion is that, in the analysed period, only extreme value theory based models provide a reasonable degree of safety while widespread VaR models do not provide adequate risk coverage and their performance is especially weak for short position in oil.

Suggested Citation

  • Sasa Zikovic, 2011. "Measuring risk of crude oil at extreme quantiles," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 29(1), pages 9-31.
  • Handle: RePEc:rfe:zbefri:v:29:y:2011:i:1:p:9-31
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    File URL: https://www.efri.uniri.hr/sites/efri.hr/files/cr-collections/2/07-zikovic-2011-1.pdf
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    References listed on IDEAS

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    1. Narayan, Paresh Kumar & Smyth, Russell, 2007. "A panel cointegration analysis of the demand for oil in the Middle East," Energy Policy, Elsevier, vol. 35(12), pages 6258-6265, December.
    2. Burnecki, Krzysztof & Misiorek, Adam & Weron, Rafal, 2010. "Loss Distributions," MPRA Paper 22163, University Library of Munich, Germany.
    3. Cologni, Alessandro & Manera, Matteo, 2008. "Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries," Energy Economics, Elsevier, vol. 30(3), pages 856-888, May.
    4. Matteo Manera & Alessandro Cologni, 2006. "The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis for the G-7 Countries," Working Papers 2006.29, Fondazione Eni Enrico Mattei.
    5. Chen, Shiu-Sheng, 2009. "Oil price pass-through into inflation," Energy Economics, Elsevier, vol. 31(1), pages 126-133, January.
    6. Coronel-Brizio, H.F. & Hernández-Montoya, A.R., 2005. "On fitting the Pareto–Levy distribution to stock market index data: Selecting a suitable cutoff value," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 437-449.
    7. Peter Ferderer, J., 1996. "Oil price volatility and the macroeconomy," Journal of Macroeconomics, Elsevier, vol. 18(1), pages 1-26.
    8. Marc Gronwald, 2008. "Large Oil Shocks and the US Economy: Infrequent Incidents with Large Effects," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 151-172.
    9. Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
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    Citations

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    Cited by:

    1. Aboura, Sofiane & Chevallier, Julien, 2016. "Spikes and crashes in the oil market," Research in International Business and Finance, Elsevier, vol. 36(C), pages 615-623.
    2. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    3. Jamshed Y. Uppal & Syeda Rabab Mudakkar, 2014. "Mitigating Vulnerability to Oil Price Risk— Applicability of Risk Models to Pakistan’s Energy Problem," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 53(3), pages 293-308.

    More about this item

    Keywords

    WTI oil; Value at Risk; VaR; extremes; extreme value theory;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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