Testing expected shortfall: an application to emerging market stock indices

Cardona, Emilio; Mora–Valencia, Andrés; Velásquez-Gaviria, Daniel

doi:10.1057/s41283-018-0046-z

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…The critical value at 5% for the normal, skew-normal, GED and SGED distributions is −0.03; for the Student's t and skewed-t distributions, it is −0.05. The critical values are sensitive to the size T, as shown in [44], and also to the degrees of freedom parameter and shape parameter, but not to the skewness parameter of the skew-normal, skewed-t distributions and SGED. As T increases, the critical value of the test increases until it stabilizes close to zero.…”

Section: Z 1 Testmentioning

confidence: 99%

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

2020

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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.

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Section: Z 1 Testmentioning

confidence: 99%

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

2020

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“…Numerous firm-level factors are assumed to cause negative shocks to stock prices, including agency problems (Bolton, Scheinkman, and Xiong 2006), tax avoidance (Kim et al, 2011), earnings management (Kirschenheiter and Melumad 2002;Leuz et al 2003), and bad news hoarding (Callen and Fang 2015). Several research studies have been conducted to determine the factors influencing stock return and enhance the firm and market risk (see, e.g., (Cardona, Mora-Valencia, and Velásquez-Gaviria 2019;Elenjical et al 2016;Ma et al 2020;Qureshi et al 2019). Bentley et al (2013) show that the different crash risk determinants depend on the firm's unique business strategy, which remains stable over a longer period.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Business strategy, market power, and stock price crash risk: Evidence from China

et al. 2021

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Business strategies play a vital role in a firm's success but, if not properly executed, can lead to financial irregularities and mispricing, influencing the firm's performance and leading to stock price crash risk. The present study examines the impact of a firm's business strategy and market power on stock price crash risk. Following Miles and Snow's ( 2003) model, we classify Chinese firms listed on the Shenzhen and Shanghai stock exchanges into defenders (conservative) and prospectors' (aggressive) business strategies over a period of 2006-2019.We employed industry and year fixed effects regression to show that prospectors who follow aggressive strategies are more prone to stock price crash risk than defenders who follow conservative strategies. Additionally, we show that firms with high market power also contribute to increased stock price crash risk. Our results are also robust to alternative control variables and different statistical models like the two-stage least squares method.

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Asymptotic Expansions for Market Risk Assessment: Evidence in Energy and Commodity Indices

Velásquez-Gaviria

Mora–Valencia

Perote

2023

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Testing expected shortfall: an application to emerging market stock indices

Cited by 6 publications

References 17 publications

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

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

Business strategy, market power, and stock price crash risk: Evidence from China

Asymptotic Expansions for Market Risk Assessment: Evidence in Energy and Commodity Indices

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