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Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures

Citations

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Cited by:

  1. Cathy W. S. Chen & Takaaki Koike & Wei‐Hsuan Shau, 2024. "Tail risk forecasting with semiparametric regression models by incorporating overnight information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1492-1512, August.
  2. Xu, Buyun & Wu, Zhimin, 2025. "Real-time GARCH@CARR: A joint model of returns, realized measure of volatility and current intraday information," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
  3. Rangika Peiris & Chao Wang & Richard Gerlach & Minh-Ngoc Tran, 2024. "Semi-parametric financial risk forecasting incorporating multiple realized measures," Papers 2402.09985, arXiv.org, revised Dec 2024.
  4. Chen, Cathy W.S. & Hsu, Hsiao-Yun & Watanabe, Toshiaki, 2023. "Tail risk forecasting of realized volatility CAViaR models," Finance Research Letters, Elsevier, vol. 51(C).
  5. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
  6. Raphael Amaro & Carlos Pinho, 2022. "Energy commodities: A study on model selection for estimating Value-at-Risk," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 5-27.
  7. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
  8. Taylor, James W., 2021. "Evaluating quantile-bounded and expectile-bounded interval forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 800-811.
  9. Kong, Manyu & Zhang, Shunqi, 2026. "Global stock market systemic risk and the asymmetric transmission to Bitcoin’s tail dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 684(C).
  10. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
  11. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
  12. Lodge, David & Pérez, Javier J. & Albrizio, Silvia & Everett, Mary & De Bandt, Olivier & Georgiadis, Georgios & Ca' Zorzi, Michele & Lastauskas, Povilas & Carluccio, Juan & Parraga Rodriguez, Susana &, 2021. "The implications of globalisation for the ECB monetary policy strategy," Occasional Paper Series 263, European Central Bank.
  13. Catania, Leopoldo & Luati, Alessandra, 2025. "Quasi Maximum Likelihood Estimation of Value at Risk and Expected Shortfall," Econometrics and Statistics, Elsevier, vol. 33(C), pages 23-34.
  14. Kuang, Wei, 2022. "The economic value of high-frequency data in equity-oil hedge," Energy, Elsevier, vol. 239(PA).
  15. Richard Gerlach & Antonio Naimoli & Giuseppe Storti, 2025. "Using quantile time series and historical simulation to forecast financial risk multiple steps ahead," Papers 2502.20978, arXiv.org, revised Mar 2025.
  16. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
  17. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
  18. Yang, Yaming, 2026. "Forecasting systemic risk measures using a dynamic semiparametric approach based on the Asymmetric Laplace distribution," International Journal of Forecasting, Elsevier, vol. 42(3), pages 989-1007.
  19. Storti, Giuseppe & Wang, Chao, 2022. "A multivariate semi-parametric portfolio risk optimization and forecasting framework," MPRA Paper 115266, University Library of Munich, Germany.
  20. Li, Dan & Clements, Adam & Drovandi, Christopher, 2023. "A Bayesian approach for more reliable tail risk forecasts," Journal of Financial Stability, Elsevier, vol. 64(C).
  21. Li, Lu & Li, Degao & Liu, Li & Tang, Linjun, 2025. "Forecasting Value-at-Risk and Expected Shortfall using penalized quantile regressions with mixed-frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 80(C).
  22. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
  23. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Combining Value-at-Risk and Expected Shortfall forecasts via the Model Confidence Set," Papers 2406.06235, arXiv.org, revised Feb 2026.
  24. 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.
  25. Giuseppe Storti & Chao Wang, 2025. "A semi-parametric dynamic conditional correlation framework for risk forecasting," Quantitative Finance, Taylor & Francis Journals, vol. 25(1), pages 31-49, January.
  26. James W. Taylor & Chao Wang, 2025. "Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall," Papers 2508.16919, arXiv.org, revised May 2026.
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