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Article

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

1
Departamento de Finanzas, Instituto Tecnológico Metropolitano-ITM, Medellín 050001, Colombia
2
School of Management, Universidad de los Andes, Bogotá 111711, Colombia
3
Department of Economics and IME, University of Salamanca (IME), 37007 Salamanca, Spain
*
Author to whom correspondence should be addressed.
Energies 2020, 13(11), 2805; https://doi.org/10.3390/en13112805
Received: 2 April 2020 / Revised: 12 May 2020 / Accepted: 29 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Mathematical and Statistical Models for Energy with Applications)
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. View Full-Text
Keywords: volatility modeling; risk and portfolio modeling; forecasting; energy volatility modeling; risk and portfolio modeling; forecasting; energy
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MDPI and ACS Style

Velásquez-Gaviria, D.; Mora-Valencia, A.; Perote, J. A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets. Energies 2020, 13, 2805. https://doi.org/10.3390/en13112805

AMA Style

Velásquez-Gaviria D, Mora-Valencia A, Perote J. A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets. Energies. 2020; 13(11):2805. https://doi.org/10.3390/en13112805

Chicago/Turabian Style

Velásquez-Gaviria, Daniel, Andrés Mora-Valencia, and Javier Perote. 2020. "A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets" Energies 13, no. 11: 2805. https://doi.org/10.3390/en13112805

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