A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets
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 shortfa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/11/2805 |
| _version_ | 1797566403072491520 |
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| author | Daniel Velásquez-Gaviria Andrés Mora-Valencia Javier Perote |
| author_facet | Daniel Velásquez-Gaviria Andrés Mora-Valencia Javier Perote |
| author_sort | Daniel Velásquez-Gaviria |
| collection | DOAJ |
| description | 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 <i>WilderHill New Energy Global Innovation</i> and the <i>Bloomberg World Energy</i> 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. |
| first_indexed | 2024-03-10T19:26:19Z |
| format | Article |
| id | doaj.art-f280fe95f9d6480d920da57ca6fea610 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:26:19Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f280fe95f9d6480d920da57ca6fea6102023-11-20T02:32:34ZengMDPI AGEnergies1996-10732020-06-011311280510.3390/en13112805A Comparison of the Risk Quantification in Traditional and Renewable Energy MarketsDaniel Velásquez-Gaviria0Andrés Mora-Valencia1Javier Perote2Departamento de Finanzas, Instituto Tecnológico Metropolitano-ITM, Medellín 050001, ColombiaSchool of Management, Universidad de los Andes, Bogotá 111711, ColombiaDepartment of Economics and IME, University of Salamanca (IME), 37007 Salamanca, SpainThe 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 <i>WilderHill New Energy Global Innovation</i> and the <i>Bloomberg World Energy</i> 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.https://www.mdpi.com/1996-1073/13/11/2805volatility modelingrisk and portfolio modelingforecastingenergy |
| spellingShingle | Daniel Velásquez-Gaviria Andrés Mora-Valencia Javier Perote A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets Energies volatility modeling risk and portfolio modeling forecasting energy |
| title | A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets |
| title_full | A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets |
| title_fullStr | A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets |
| title_full_unstemmed | A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets |
| title_short | A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets |
| title_sort | comparison of the risk quantification in traditional and renewable energy markets |
| topic | volatility modeling risk and portfolio modeling forecasting energy |
| url | https://www.mdpi.com/1996-1073/13/11/2805 |
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