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Forecasting risk via realized GARCH, incorporating the realized range

Citations

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Cited by:

  1. 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).
  2. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
  3. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
  4. Zhengkun Li & Minh-Ngoc Tran & Chao Wang & Richard Gerlach & Junbin Gao, 2020. "A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting," Papers 2001.08374, arXiv.org, revised May 2021.
  5. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
  6. Liu, Chen & Wang, Chao & Tran, Minh-Ngoc & Kohn, Robert, 2025. "A long short-term memory enhanced realized conditional heteroskedasticity model," Economic Modelling, Elsevier, vol. 142(C).
  7. Nguyen, Hien Thi & Nguyen, Hoang & Tran, Minh-Ngoc, 2024. "Deep learning enhanced volatility modeling with covariates," Finance Research Letters, Elsevier, vol. 69(PB).
  8. Chao Wang & Richard Gerlach, 2021. "A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting," Papers 2106.00288, arXiv.org, revised Oct 2022.
  9. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
  10. Jinguan Lin & Yizhi Mao & Hongxia Hao & Guangying Liu, 2025. "Semiparametric Estimation and Application of Realized GARCH Model with Time-Varying Leverage Effect," Mathematics, MDPI, vol. 13(9), pages 1-26, May.
  11. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
  12. Yuta Kurose, 2022. "Bayesian GARCH modeling for return and range," Economics Bulletin, AccessEcon, vol. 42(3), pages 1717-1727.
  13. 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.
  14. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
  15. Chao Wang & Qian Chen & Richard Gerlach, 2017. "Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution," Papers 1707.03715, arXiv.org.
  16. Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
  17. Chao Wang & Richard Gerlach & Qian Chen, 2018. "A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework," Papers 1807.02422, arXiv.org, revised Jan 2021.
  18. Ren, Xiaohang & He, Yue & Liu, Chuanwang & Tao, Lizhu, 2025. "Extreme risk spillovers between SC, WTI and Brent crude oil futures-Evidence from time-varying Granger causality test," Energy, Elsevier, vol. 320(C).
  19. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
  20. Chen Liu & Chao Wang & Minh-Ngoc Tran & Robert Kohn, 2023. "Deep Learning Enhanced Realized GARCH," Papers 2302.08002, arXiv.org, revised Oct 2023.
  21. Didit Budi Nugroho & Adi Setiawan & Takayuki Morimoto, 2025. "Modelling and Forecasting Financial Volatility with Realized GARCH Model: A Comparative Study of Skew- t Distributions Using GRG and MCMC Methods," Econometrics, MDPI, vol. 13(3), pages 1-27, September.
  22. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
  23. Qianli Zhao & Chao Wang & Richard Gerlach & Giuseppe Storti & Lingxiang Zhang, 2024. "Autoencoder Enhanced Realised GARCH on Volatility Forecasting," Papers 2411.17136, arXiv.org.
  24. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
  25. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.
  26. Richard Gerlach & Chao Wang, 2018. "Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures," Papers 1805.08653, arXiv.org.
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