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Tail Risk Forecasting Using Bayesian Realized Egarch Models

Tendenan, Vica; Gerlach, Richard H.; Wang, Chao - 2022

This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model and adopts a standardized Student-t and a standardized skewed Student-t distributions for the return equation. The Bayesian estimators show more...

Persistent link: https://www.econbiz.de/10014239179

A long short-term memory enhanced realized conditional heteroskedasticity model

Liu, Chen; Wang, Chao; Minh-Ngoc Tran; Kohn, Robert - In: Economic modelling 142 (2025), pp. 1-10

Persistent link: https://www.econbiz.de/10015192384

A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures

Wang, Chao; Gerlach, Richard; Chen, Qian - In: Quantitative finance 23 (2023) 2, pp. 309-334

Persistent link: https://www.econbiz.de/10014232647

Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution

Wang, Chao; Chen, Qian; Gerlach, Richard - In: Quantitative finance 19 (2019) 6, pp. 1017-1042

Persistent link: https://www.econbiz.de/10012194739

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

Gerlach, Richard; Wang, Chao - In: International journal of forecasting 36 (2020) 2, pp. 489-506

Persistent link: https://www.econbiz.de/10012415185

A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting

Wang, Chao; Gerlach, Richard - In: Journal of forecasting 43 (2024) 1, pp. 40-57

Persistent link: https://www.econbiz.de/10014443184