Estimating ‘Value at Risk’ of crude oil price and its spillover effect using the GED-GARCH approach

By Ying Fan, Yue‐Jun Zhang, Hsien-Tang Tsai, Yi-Ming Wei. 2008

222 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/092e7cbab146f066a5d73fc92baea189b8137571

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Yue‐Jun Zhang
2. Ruijin Du
3. Minggang Wang
4. H. Eugene Stanley
5. Sang Hoon Kang
6. Lin Chen
7. Chao Wang
8. Seong‐Min Yoon

In Finance

1. Seong‐Min Yoon
2. Lu Zhang
3. Sang Hoon Kang
4. Yue‐Jun Zhang
5. Ting Yao
6. Kaijian He
7. Chih-Chiang Wu
8. Yu Chang

Citations in the corpus

• Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models? (2019, methodology): “, MMGARCH and MRS-GARCH) models. The empirical results show that the regime switching GARCH models can help us identify the structural changes in volatility. Meanwhile, in the in-sample estimation, the MRS-GARCH model displays superiority in weekly data compared to the single-regime GARCH and MMGARC”
• A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “[12] introduced GARCH type models based on generalized error distribution (GED) to examine the risk spillover effect between West Texas Intermediate (WTI) and Brent crude oil markets.”
• Modeling energy price dynamics: GARCH versus stochastic volatility (2016, background): “, Fong and See (2001), Sadorsky (2006), Kang, Kang, and Yoon (2009), Agnolucci (2009), Mohammadi and Su (2010), Nomikos and Andriosopoulos (2012), Mason and Wilmot (2014) and Manera, Nicolini, and Vignati (2014).”
• Interpreting the crude oil price movements: Evidence from the Markov regime switching model (2015, methodology): “[12] GED-GARCH The GED-GARCH-based VaR approach appears more effective than the well-recognized HSAF (i.”
• The economic value of co-movement between oil price and exchange rate using copula-based GARCH models (2011, background): “4 Examples include Giot and Laurent (2003), Patton (2006), and Fan et al. (2008). the US dollar depreciates as compared to when the US dollar appreciates, especially after 2002.”
• Modeling and forecasting the volatility of petroleum futures prices (2013, methodology): “For example, (1) an appropriate volatility model provides accuracy for capital reserve requirements in quantifying value-atrisk estimations (Fan et al., 2008; Aloui and Mabrouk, 2010), (2) accurate conditional variance from the volatility model is used for calculating hedging ratios and enhancing he”
• Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models (2020, methodology): “…produce more accurate forecasts than traditional multivariate GARCH; while some of the traditional GARCH models are limited to modelling extreme cases of risk spillover, such as the GED-GARCH model (Fan, 2008); or require more parameters for estimation, such as the DCC-MECGARCH model (Tsuji, 2018).”