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Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach

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  1. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
  2. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
  3. Ren, Xiaohang & Li, Yiying & Sun, Xianming & Bu, Ruijun & Jawadi, Fredj, 2023. "Modeling extreme risk spillovers between crude oil and Chinese energy futures markets," Energy Economics, Elsevier, vol. 126(C).
  4. Mokni, Khaled & Youssef, Manel, 2019. "Measuring persistence of dependence between crude oil prices and GCC stock markets: A copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 14-33.
  5. Laporta, Alessandro G. & Merlo, Luca & Petrella, Lea, 2018. "Selection of Value at Risk Models for Energy Commodities," Energy Economics, Elsevier, vol. 74(C), pages 628-643.
  6. Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021. "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, vol. 93(C).
  7. Amaro, Raphael & Pinho, Carlos & Madaleno, Mara, 2022. "Forecasting the Value-at-Risk of energy commodities: A comparison of models and alternative distribution functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 77-101.
  8. Onder Buberkoku, 2018. "Examining the Value-at-risk Performance of Fractionally Integrated GARCH Models: Evidence from Energy Commodities," International Journal of Economics and Financial Issues, Econjournals, vol. 8(3), pages 36-50.
  9. Zi‐Yi Guo, 2020. "Stochastic multifactor models in risk management of energy futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(12), pages 1918-1934, December.
  10. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
  11. Yanqiong Liu & Zhenghui Li & Yanyan Yao & Hao Dong, 2021. "Asymmetry of Risk Evolution in Crude Oil Market: From the Perspective of Dual Attributes of Oil," Energies, MDPI, vol. 14(13), pages 1-22, July.
  12. Rangga Handika & Rangga Handika & Sigit Triandaru, 2016. "Is the Best Generalized Autoregressive Conditional Heteroskedasticity(p,q) Value-at-risk Estimate also the Best in Reality? An Evidence from Australian Interconnected Power Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 6(4), pages 814-821.
  13. Ra l De Jes s Guti rrez & Lidia E. Carvajal Guti rrez & Oswaldo Garcia Salgado, 2023. "Value at Risk and Expected Shortfall Estimation for Mexico s Isthmus Crude Oil Using Long-Memory GARCH-EVT Combined Approaches," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 467-480, July.
  14. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
  15. Halkos, George & Tsirivis, Apostolos, 2019. "Using Value-at-Risk for effective energy portfolio risk management," MPRA Paper 91674, University Library of Munich, Germany.
  16. Xiao, Yang, 2020. "The risk spillovers from the Chinese stock market to major East Asian stock markets: A MSGARCH-EVT-copula approach," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 173-186.
  17. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
  18. Chen, Liyuan & Zerilli, Paola & Baum, Christopher F., 2019. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Energy Economics, Elsevier, vol. 79(C), pages 111-129.
  19. Wei Kuang, 2022. "Oil tail-risk forecasts: from financial crisis to COVID-19," Risk Management, Palgrave Macmillan, vol. 24(4), pages 420-460, December.
  20. Bangzhu Zhu & Shunxin Ye & Kaijian He & Julien Chevallier & Rui Xie, 2019. "Measuring the risk of European carbon market: an empirical mode decomposition-based value at risk approach," Annals of Operations Research, Springer, vol. 281(1), pages 373-395, October.
  21. František Čech & Jozef Baruník, 2019. "Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1167-1189, September.
  22. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
  23. Gkillas, Konstantinos & Konstantatos, Christoforos & Papathanasiou, Spyros & Wohar, Mark, 2023. "Estimation of value at risk for copper," Journal of Commodity Markets, Elsevier, vol. 32(C).
  24. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Value-at-risk methodologies for effective energy portfolio risk management," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 197-212.
  25. Arian, Hamid & Moghimi, Mehrdad & Tabatabaei, Ehsan & Zamani, Shiva, 2022. "Encoded Value-at-Risk: A machine learning approach for portfolio risk measurement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 500-525.
  26. Zi-Yi Guo & Yangxiaoteng Luo, 2017. "Dynamic Stochastic Factors, Risk Management and the Energy Futures," International Business Research, Canadian Center of Science and Education, vol. 10(9), pages 50-59, September.
  27. Lu Yang & Shigeyuki Hamori, 2020. "Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach," Energies, MDPI, vol. 13(14), pages 1-27, July.
  28. Maitra, Debasish & Guhathakurta, Kousik & Kang, Sang Hoon, 2021. "The good, the bad and the ugly relation between oil and commodities: An analysis of asymmetric volatility connectedness and portfolio implications," Energy Economics, Elsevier, vol. 94(C).
  29. Alkathery, Mohammed A. & Chaudhuri, Kausik & Nasir, Muhammad Ali, 2022. "Implications of clean energy, oil and emissions pricing for the GCC energy sector stock," Energy Economics, Elsevier, vol. 112(C).
  30. Zhang, Heng-Guo & Su, Chi-Wei & Song, Yan & Qiu, Shuqi & Xiao, Ran & Su, Fei, 2017. "Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model," Economic Modelling, Elsevier, vol. 67(C), pages 355-367.
  31. Manel Youssef & Khaled Mokni, 2019. "Do Crude Oil Prices Drive the Relationship between Stock Markets of Oil-Importing and Oil-Exporting Countries?," Economies, MDPI, vol. 7(3), pages 1-22, July.
  32. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
  33. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2020. "Risk quantification for commodity ETFs: Backtesting value-at-risk and expected shortfall," International Review of Financial Analysis, Elsevier, vol. 70(C).
  34. Krzysztof Echaust & Małgorzata Just, 2021. "Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic," Energies, MDPI, vol. 14(14), pages 1-21, July.
  35. Lyu, Yongjian & Qin, Fanshu & Ke, Rui & Wei, Yu & Kong, Mengzhen, 2024. "Does mixed frequency variables help to forecast value at risk in the crude oil market?," Resources Policy, Elsevier, vol. 88(C).
  36. Westgaard, Sjur & Fleten, Stein-Erik & Negash, Ahlmahz & Botterud, Audun & Bogaard, Katinka & Verling, Trude Haugsvaer, 2021. "Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market," Energy, Elsevier, vol. 214(C).
  37. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2019. "Quantifying Risk in Traditional Energy and Sustainable Investments," Sustainability, MDPI, vol. 11(3), pages 1-22, January.
  38. Zhang, Yue-Jun & Chen, Ming-Ying, 2018. "Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function," European Journal of Operational Research, Elsevier, vol. 269(1), pages 64-78.
  39. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.
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