Bayesian CV@R/super-quantile regression

Tsionas, Efthymios G.; Izzeldin, Marwan

doi:10.1080/02664763.2018.1450363

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…(2014)[ 11 ] presented a super-quantile regression with several numerical examples in the area of uncertainty quantification. Tsionas and Izzeldin (2018)[ 12 ] provided the Bayesian interpretation of the conditional value at risk; that is, super-quantile regression and computations are based on particle filtering using a special posterior distribution consistent with the super-quartile concept.…”

Section: S Ubjects and M Ethodsmentioning

confidence: 99%

Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study

Vijayaragunathan,

John,

Srinivasan

2023

Indian Journal of Community Medicine

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Background: In this article, we attempt to demonstrate the superiority of the Bayesian approach over the frequentist approaches of the multiple linear regression model in identifying the influencing factors for the response variable. Methods and Material: A survey was conducted among the 310 respondents from the Kathirkamam area in Puducherry. We have considered the response variable, body mass index (BMI), and the predictors such as age, weight, gender, nature of the job, and marital status of individuals were collected with the personal interview method. Jeffreys’s Amazing Statistics Program (JASP) software was used to analyze the dataset. In the conventional multiple linear regression model, the single value of regression coefficients is determined, while in the Bayesian linear regression model, the regression coefficient of each predictor follows a specific posterior distribution. Furthermore, it would be most useful to identify the best models from the list of possible models with posterior probability values. An inclusion probability for all the predictors will give a superior idea of whether the predictors are included in the model with probability. Results and Conclusions: The Bayesian framework offers a wide range of results for the regression coefficients instead of the single value of regression coefficients in the frequentist test. Such advantages of the Bayesian approach will catapult the quality of investigation outputs by giving more reliability to solutions of scientific problems.

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Section: S Ubjects and M Ethodsmentioning

confidence: 99%

Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study

Vijayaragunathan,

John,

Srinivasan

2023

Indian Journal of Community Medicine

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“…In related applications, Ref. [29] employed Bayesian CVaR super-quantile regression on the energy price dataset, whereas Ref. [30] used ES and VaR as risk measures in a real options context.…”

Section: Literature Review: Risk Quantificationmentioning

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

Velásquez¹,

Mora–Valencia²,

Perote³

2020

SSRN Journal

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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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Bayesian CV@R/super-quantile regression

Cited by 3 publications

References 35 publications

Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study

Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study

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

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

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