| Forecasting risk via realized GARCH, incorporating the realized range R Gerlach, C Wang Quantitative Finance 16 (4), 501-511, 2016 | 53 | 2016 |
| Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures R Gerlach, C Wang International Journal of Forecasting 36 (2), 489-506, 2020 | 37 | 2020 |
| Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution C Wang, Q Chen, R Gerlach Quantitative Finance 19 (6), 1017-1042, 2019 | 23 | 2019 |
| Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility R Gerlach, D Walpole, C Wang Quantitative Finance 17 (2), 199-215, 2017 | 18 | 2017 |
| Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles G Storti, C Wang International Journal of Forecasting. In press., 2021 | 13 | 2021 |
| Bayesian semi-parametric realized conditional autoregressive expectile models for tail risk forecasting R Gerlach, C Wang Journal of Financial Econometrics 20 (1), 105-138, 2022 | 12 | 2022 |
| A semi-parametric realized joint value-at-risk and expected shortfall regression framework C Wang, R Gerlach, Q Chen arXiv preprint arXiv:1807.02422, 2018 | 9 | 2018 |
| A Bayesian long short-term memory model for value at risk and expected shortfall joint forecasting Z Li, MN Tran, C Wang, R Gerlach, J Gao arXiv preprint arXiv:2001.08374, 2020 | 8 | 2020 |
| Bayesian semi-parametric realized-care models for tail risk forecasting incorporating realized measures R Gerlach, C Wang arXiv preprint arXiv:1612.08488, 2016 | 6 | 2016 |
| A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures C Wang, R Gerlach, Q Chen Quantitative Finance 23 (2), 309-334, 2023 | 4 | 2023 |
| Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach G Storti, C Wang Journal of Forecasting. In press., 2021 | 4 | 2021 |
| Realized recurrent conditional heteroskedasticity model for volatility modelling C Liu, C Wang, M Tran, R Kohn | 2 | 2023 |
| Bayesian semi-parametric realized-care models for tail risk forecasting incorporating range and realized measures R Gerlach, C Wang Business Analytics., 2015 | 2 | 2015 |
| Seasonality in deep learning forecasts of electricity imbalance prices S Deng, J Inekwe, V Smirnov, A Wait, C Wang Energy Economics 137, 107770, 2024 | 1 | 2024 |
| A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting C Wang, R Gerlach Journal of Forecasting 43 (1), 40-57, 2024 | 1 | 2024 |
| A multivariate semi-parametric portfolio risk optimization and forecasting framework G Storti, C Wang arXiv preprint arXiv:2207.04595, 2022 | 1 | 2022 |
| Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall C Wang, R Gerlach arXiv preprint arXiv:1906.09961, 2019 | 1 | 2019 |
| Graph Signal Processing for Global Stock Market Volatility Forecasting Z Chi, J Gao, C Wang arXiv preprint arXiv:2410.22706, 2024 | | 2024 |
| Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Days Z Chi, J Gao, C Wang arXiv preprint arXiv:2409.15320, 2024 | | 2024 |
| DeepVol: A Deep Transfer Learning Approach for Universal Asset Volatility Modeling C Liu, MN Tran, C Wang, R Gerlach, R Kohn arXiv preprint arXiv:2309.02072, 2023 | | 2023 |
Richard GerlachThe University of SydneyVerified email at sydney.edu.au
