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Forecast combinations for value at risk and expected shortfall

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Cited by:

  1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
  2. Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.
  3. Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022. ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers 202215, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
  4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Nowcasting tail risk to economic activity at a weekly frequency," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 843-866, August.
  5. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2022. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," International Journal of Forecasting, Elsevier, vol. 38(1), pages 97-116.
  6. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
  7. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
  8. Bonaccolto, Giovanni & Caporin, Massimiliano & Maillet, Bertrand B., 2022. "Dynamic large financial networks via conditional expected shortfalls," European Journal of Operational Research, Elsevier, vol. 298(1), pages 322-336.
  9. Vidal-Llana, Xenxo & Guillén, Montserrat, 2022. "Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
  10. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
  11. Będowska-Sójka, Barbara & Kliber, Agata, 2021. "Information content of liquidity and volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
  12. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
  13. d’Addona, Stefano & Khanom, Najrin, 2022. "Estimating tail-risk using semiparametric conditional variance with an application to meme stocks," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 241-260.
  14. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
  15. Yuruixian Zhang & Wei Chong Choo & Yuhanis Abdul Aziz & Choy Leong Yee & Jen Sim Ho, 2022. "Go Wild for a While? A Bibliometric Analysis of Two Themes in Tourism Demand Forecasting from 1980 to 2021: Current Status and Development," Data, MDPI, vol. 7(8), pages 1-38, July.
  16. Giuseppe Storti & Chao Wang, 2021. "Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach," Papers 2104.04918, arXiv.org, revised Jul 2021.
  17. Tomlinson, Matthew F. & Greenwood, David & Mucha-Kruczyński, Marcin, 2024. "2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log returns: Out-of-sample comparison of conditional EVT models," International Journal of Forecasting, Elsevier, vol. 40(1), pages 324-347.
  18. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
  19. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
  20. Verena Monschang & Bernd Wilfling, 2022. "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers 9722, Center for Quantitative Economics (CQE), University of Muenster.
  21. Kshitij Kakade & Aswini Kumar Mishra & Kshitish Ghate & Shivang Gupta, 2022. "Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH‐LSTM based Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 103-117, April.
  22. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
  23. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.
  24. Man Wang & Yihan Cheng, 2022. "Forecasting value at risk and expected shortfall using high‐frequency data of domestic and international stock markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1595-1607, December.
  25. Andrei Rusu, 2020. "Multivariate VaR: A Romanian Market study," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 12(1), pages 79-95, June.
  26. Lei, Heng & Xue, Minggao & Liu, Huiling & Ye, Jing, 2023. "Precious metal as a safe haven for global ESG stocks: Portfolio implications for socially responsible investing," Resources Policy, Elsevier, vol. 80(C).
  27. Md Akhtaruzzaman & Ramzi Benkraiem & Sabri Boubaker & Constantin Zopounidis, 2022. "COVID‐19 crisis and risk spillovers to developing economies: Evidence from Africa," Journal of International Development, John Wiley & Sons, Ltd., vol. 34(4), pages 898-918, May.
  28. Giampiero Gallo & Ostap Okhrin & Giuseppe Storti, 2024. "Dynamic tail risk forecasting: what do realized skewness and kurtosis add?," Papers 2409.13516, arXiv.org.
  29. Lazar, Emese & Pan, Jingqi & Wang, Shixuan, 2024. "On the estimation of Value-at-Risk and Expected Shortfall at extreme levels," Journal of Commodity Markets, Elsevier, vol. 34(C).
  30. Ord, J. Keith, 2022. "The uncertainty track: Machine learning, statistical modeling, synthesis," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1526-1530.
  31. Almeida, José & Soares, Joao & Lezama, Fernando & Vale, Zita & Francois, Bruno, 2024. "Comparison of evolutionary algorithms for solving risk-based energy resource management considering conditional value-at-risk analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 87-110.
  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. Huang, Yujun, 2024. "Do ESG ETFs provide downside risk protection during Covid-19? Evidence from forecast combination models," International Review of Financial Analysis, Elsevier, vol. 94(C).
  34. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
  35. Zhao, Lu-Tao & Wang, Dai-Song & Ren, Zhong-Yuan, 2024. "The impact of joint events on oil price volatility: Evidence from a dynamic graphical news analysis model," Economic Modelling, Elsevier, vol. 130(C).
  36. Su, Kuangxi & Yao, Yinhong & Zheng, Chengli & Xie, Wenzhao, 2023. "A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 35-50.
  37. Giuseppe Storti & Chao Wang, 2023. "Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1648-1663, November.
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