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A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets

Author

Listed:
  • Daniel Velásquez-Gaviria

    (Departamento de Finanzas, Instituto Tecnológico Metropolitano-ITM, Medellín 050001, Colombia)

  • Andrés Mora-Valencia

    (School of Management, Universidad de los Andes, Bogotá 111711, Colombia)

  • Javier Perote

    (Department of Economics and IME, University of Salamanca (IME), 37007 Salamanca, Spain)

Abstract

The transition from traditional energy to cleaner energy sources has raised concerns from companies and investors regarding, among other things, the impact on financial downside risk. This article implements backtesting techniques to estimate and validate the value-at-risk (VaR) and expected shortfall (ES) in order to compare their performance among four renewable energy stocks and four traditional energy stocks from the WilderHill New Energy Global Innovation and the Bloomberg World Energy for the period 2005-2016. The models used to estimate VaR and ES are AR(1)-GARCH(1,1), AR(1)-EGARCH(1,1), and AR(1)-APARCH(1,1), all of them under either normal, skew-normal, Student’s t, skewed-t, Generalized Error or Skew-Generalized Error distributed innovations. Backtesting performance is tested through traditional Kupiec and Christoffersen tests for VaR, but also through recent backtesting ES techniques. The paper extends these tests to the skewed-t, skew-normal and Skew-Generalized Error distributions and applies it for the first time in traditional and renewable energy markets showing that the skewed-t and the Generalized Error distribution are an accurate tool for risk management in those markets. Our findings have important implications for portfolio managers and regulators in terms of capital allocation in renewable and traditional energy stocks, mainly to reduce the impact of possible extreme loss events.

Suggested Citation

  • Daniel Velásquez-Gaviria & Andrés Mora-Valencia & Javier Perote, 2020. "A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets," Energies, MDPI, vol. 13(11), pages 1-42, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2805-:d:366071
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    as
    1. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2020. "Risk quantification for commodity ETFs: Backtesting value-at-risk and expected shortfall," International Review of Financial Analysis, Elsevier, vol. 70(C).
    2. Emilio Cardona & Andrés Mora-Valencia & Daniel Velásquez-Gaviria, 2019. "Testing expected shortfall: an application to emerging market stock indices," Risk Management, Palgrave Macmillan, vol. 21(3), pages 153-182, September.
    3. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    4. Mike G. Tsionas & Marwan Izzeldin, 2018. "Bayesian CV@R/super-quantile regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(16), pages 2943-2957, December.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. C. W. J. Granger & Zhuanxin Ding, 1995. "Some Properties of Absolute Return: An Alternative Measure of Risk," Annals of Economics and Statistics, GENES, issue 40, pages 67-91.
    7. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    8. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    9. Tatiana Ermolieva & Petr Havlík & Yuri Ermoliev & Aline Mosnier & Michael Obersteiner & David Leclère & Nikolay Khabarov & Hugo Valin & Wolf Reuter, 2016. "Integrated Management of Land Use Systems under Systemic Risks and Security Targets: A Stochastic Global Biosphere Management Model," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(3), pages 584-601, September.
    10. Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
    11. Wen, Xiaoqian & Nguyen, Duc Khuong, 2017. "Can investors of Chinese energy stocks benefit from diversification into commodity futures?," Economic Modelling, Elsevier, vol. 66(C), pages 184-200.
    12. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    13. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    14. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    15. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    16. Wu, Fei, 2019. "Sectoral contributions to systemic risk in the Chinese stock market," Finance Research Letters, Elsevier, vol. 31(C).
    17. Feng, Zhen-Hua & Wei, Yi-Ming & Wang, Kai, 2012. "Estimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETS," Applied Energy, Elsevier, vol. 99(C), pages 97-108.
    18. Pisciella, P. & Vespucci, M.T. & Bertocchi, M. & Zigrino, S., 2016. "A time consistent risk averse three-stage stochastic mixed integer optimization model for power generation capacity expansion," Energy Economics, Elsevier, vol. 53(C), pages 203-211.
    19. Spada, Matteo & Paraschiv, Florentina & Burgherr, Peter, 2018. "A comparison of risk measures for accidents in the energy sector and their implications on decision-making strategies," Energy, Elsevier, vol. 154(C), pages 277-288.
    20. repec:adr:anecst:y:1995:i:40:p:04 is not listed on IDEAS
    21. Jim Hanly, 2017. "Managing Energy Price Risk using Futures Contracts: A Comparative Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    22. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    23. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    24. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    25. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
    26. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    27. Esther B. Del Brio & Andrés Mora-Valencia & Javier Perote, 2019. "Expected shortfall assessment in commodity (L)ETF portfolios with semi-nonparametric specifications," The European Journal of Finance, Taylor & Francis Journals, vol. 25(17), pages 1746-1764, November.
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