Estimation of value-at-risk for energy commodities via fat-tailed GARCH models

The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR e...

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Published in	Energy economics Vol. 30; no. 3; pp. 1173 - 1191
Main Authors	Hung, Jui-Cheng, Lee, Ming-Chih, Liu, Hung-Chun
Format	Journal Article
Language	English
Published	Amsterdam Elsevier B.V 01.05.2008 Elsevier Science Elsevier Elsevier Science Ltd
Series	Energy Economics
Subjects	Applied sciences Commodities Comparative analysis Crude oil Econometric models Economic data Energy Energy commodities Energy economics Energy market Estimating techniques Estimation Exact sciences and technology Fat tails Fossil fuels and derived products GARCH models GARCH-HT General, economic and professional studies Innovations Methodology. Modelling MRSB Oil Stochastic models Studies VaR Vector-autoregressive models USA, New York, New York, New York Harbor C53 MRSB C52 Energy commodities VaR G15 Fat tails GARCH-HT Performance evaluation Volatility Econometric model C52; C53; G15 Fuel oil Petroleum Return on investment GARCH modele VaR; GARCH-HT; Energy commodities; MRSB; Fat tails Gasoline Distribution function Profit Spot price Risk management Portfolio management Propane
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Abstract	The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR estimates using three GARCH models (GARCH- N, GARCH- t and GARCH-HT). Daily spot prices of five energy commodities (WTI crude oil, Brent crude oil, heating oil #2, propane and New York Harbor Conventional Gasoline Regular) are used to compare the accuracy and efficiency of the VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Additionally, MRSB indicates that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation.
AbstractList	The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR estimates using three GARCH models (GARCH-N, GARCH-t and GARCH-HT). Daily spot prices of five energy commodities (WTI crude oil, Brent crude oil, heating oil #2, propane and New York Harbor Conventional Gasoline Regular) are used to compare the accuracy and efficiency of the VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Additionally, MRSB indicates that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation. Influence of fat-tailed innovation process on the performance of one-day-ahead Value-at-Risk (VaR) estimates using three GARCH models is investigated. It suggests that the VaR forecasts obtained by the GARCH-HT model provide more satisfactory results in both accurate and efficient concerns as a whole. On the aspect of statistical significance, the superiority test indicates that models that succeed in accuracy tests are found to be indifferent with the competing models when risk managers employ a quadratic loss function to reflect their preferences. Findings imply that fat tails in return innovation process indeed play an important role in VaR estimates and should be considered in risk management. The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR estimates using three GARCH models (GARCH-N, GARCH-t and GARCH-HT). Daily spot prices of five energy commodities (WTI crude oil, Brent crude oil, heating oil #2, propane and New York Harbor Conventional Gasoline Regular) are used to compare the accuracy and efficiency of the VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Additionally, MRSB indicates that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation. [PUBLICATION ABSTRACT] The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR estimateis using three GARCH models (GARCH-N, GARCH-t and GARCH-HT). Daily spot prices of five energy commodities (WTI crude oil, Brent crude oil, heating oil #2, propane and New York Harbor Conventional Gasoline Regular) are used to compare the accuracy and efficiency of the VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Additionally, MRSB indicates that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation. The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR estimates using three GARCH models (GARCH-N, GARCH-t and GARCH-HT). Daily spot prices of five energy commodities (WTI crude oil, Brent crude oil, heating oil #2, propane and New York Harbor Conventional Gasoline Regular) are used to compare the accuracy and efficiency of the VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Additionally, MRSB indicates that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation. All rights reserved, Elsevier The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation quality of the required quantiles. This study investigates the influence of fat-tailed innovation process on the performance of one-day-ahead VaR estimates using three GARCH models (GARCH- N, GARCH- t and GARCH-HT). Daily spot prices of five energy commodities (WTI crude oil, Brent crude oil, heating oil #2, propane and New York Harbor Conventional Gasoline Regular) are used to compare the accuracy and efficiency of the VaR models. Empirical results suggest that for asset returns that exhibit leptokurtic and fat-tailed features, the VaR estimates generated by the GARCH-HT models have good accuracy at both low and high confidence levels. Additionally, MRSB indicates that the GARCH-HT model is more efficient than alternatives for most cases at high confidence levels. These findings suggest that the heavy-tailed distribution is more suitable for energy commodities, particularly VaR calculation.
Author	Lee, Ming-Chih Hung, Jui-Cheng Liu, Hung-Chun
Author_xml	– sequence: 1 givenname: Jui-Cheng surname: Hung fullname: Hung, Jui-Cheng email: hongrc@mail.ypu.edu.tw organization: Department of Finance, Yuanpei University, N0.306. Yuanpei St. Hsin Chu 300, Taiwan – sequence: 2 givenname: Ming-Chih surname: Lee fullname: Lee, Ming-Chih organization: Department of Banking & Finance, Tamkang University, 151 Ying-Chuan Road, Tamsui 251, Taipei County, Taiwan – sequence: 3 givenname: Hung-Chun surname: Liu fullname: Liu, Hung-Chun organization: Department of Banking & Finance, Tamkang University, 151 Ying-Chuan Road, Tamsui 251, Taipei County, Taiwan
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Keywords	C53 MRSB C52 Energy commodities VaR G15 Fat tails GARCH-HT Performance evaluation Volatility Econometric model C52; C53; G15 Fuel oil Petroleum Return on investment GARCH modele VaR; GARCH-HT; Energy commodities; MRSB; Fat tails Gasoline Distribution function Profit Spot price Risk management Portfolio management Propane
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Snippet	The choice of an appropriate distribution for return innovations is important in VaR applications owing to its ability to directly affect the estimation... Influence of fat-tailed innovation process on the performance of one-day-ahead Value-at-Risk (VaR) estimates using three GARCH models is investigated. It...
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SubjectTerms	Applied sciences Commodities Comparative analysis Crude oil Econometric models Economic data Energy Energy commodities Energy economics Energy market Estimating techniques Estimation Exact sciences and technology Fat tails Fossil fuels and derived products GARCH models GARCH-HT General, economic and professional studies Innovations Methodology. Modelling MRSB Oil Stochastic models Studies VaR Vector-autoregressive models
Title	Estimation of value-at-risk for energy commodities via fat-tailed GARCH models
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