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

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.

The impact of the El Niño phenomenon on electricity prices in hydrologic‐based production systems: A switching regime semi‐nonparametric approach

“…Mauleon and Perote 30 also used the SNP distribution to model the stock market in the United States and the United Kingdom, while Ñíguez and Perote 31 did so to evaluate the stock performance of the United States. This SNP approach has been applied to modeling many other series in the last years—for example, Del Brio et al 32–34 and Cortés et al 35,36 The comparison with other parametric or nonparametric approaches is also left for further research (see, e.g., Velásquez‐Gaviria et al 37 ), but as far as we know and despite its clear advantages against Gaussian assumptions, it has only been used to model electricity markets by Trespalacios et al 38,39 …”

“…Mauleon and Perote 30 also used the SNP distribution to model the stock market in the United States and the United Kingdom, while Ñíguez and Perote 31 did so to evaluate the stock performance of the United States. This SNP approach has been applied to modeling many other series in the last years-for example, Del Brio et al [32][33][34] and Cortés et al 35,36 The comparison with other parametric or nonparametric approaches is also left for further research (see, e.g., Velásquez-Gaviria et al 37 ), but as far as we know and despite its clear advantages against Gaussian assumptions, it has only been used to model electricity markets by Trespalacios et al 38,39 This paper proposes a spot price model that considers seasonality, mean reversion, asymmetry, kurtosis, and other distribution moments. Although there are a bunch of papers modeling electricity spot prices with regime jumps-see, for example, Huisman and Mahieu, 40 Weron et al, 41 or Haldrup and Nielsen 42 -none of them is built upon the basis of the SNP approach.…”

Comparing gold’s and Bitcoin’s safe-haven roles against energy commodities during the COVID-19 outbreak: A vine copula approach

“…Accordingly, investors are motivated to prepare capital allocation for bearing a worse extreme downside risk ( Bredin et al, 2017 ). The amount of such capital may be determined by forecasting an accurate measure of downside risk (see, e.g., Del Brio et al, 2020 , Velásquez-Gaviria et al, 2020 , Syuhada et al, 2021 ) as in financial risk management.…”

Editorial and Ideas for Research Using Mathematical and Statistical Models for Energy with Applications

“…They then use their model to analyze an electricity market in the Colombian context, which is affected by the El Niño phenomenon, which they find affecting the decision to sell their wind-generated energy to the grid. Velásquez-Gaviria [8] employ the traditional Kupiec and Christoffersen tests, and the recent back-testing expected shortfall (ES) techniques to estimate and validate the value-at-risk (VaR) and the ES. They apply the AR(1)-GARCH(1,1), AR(1)-EGARCH(1,1), and AR(1)-APARCH(1,1) models with innovations under different distribution scenarios.…”