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 inEnergy economics Vol. 30; no. 3; pp. 1173 - 1191
Main Authors Hung, Jui-Cheng, Lee, Ming-Chih, Liu, Hung-Chun
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.05.2008
Elsevier Science
Elsevier
Elsevier Science Ltd
SeriesEnergy 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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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0140-9883
1873-6181
DOI:10.1016/j.eneco.2007.11.004