Similar Search Results

Narrow search

Bayesian semi-parametric realized conditional autoregressive expectile models for tail risk forecasting

Gerlach, Richard; Wang, Chao - In: Journal of financial econometrics 20 (2022) 1, pp. 105-138

Persistent link: https://www.econbiz.de/10012878188

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 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

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

Investor sentiment and stock volatility : new evidence

Gong, Xue; Zhang, Weiguo; Wang, Junbo; Wang, Chao - In: International review of financial analysis 80 (2022), pp. 1-17

Persistent link: https://www.econbiz.de/10013366314