| Inference on self‐exciting jumps in prices and volatility using high‐frequency measures W Maneesoonthorn, CS Forbes, GM Martin Journal of Applied Econometrics 32 (3), 504-532, 2017 | 40 | 2017 |
| Time series copulas for heteroskedastic data R Loaiza‐Maya, MS Smith, W Maneesoonthorn Journal of Applied Econometrics 33 (3), 332-354, 2018 | 38 | 2018 |
| Auxiliary likelihood-based approximate Bayesian computation in state space models GM Martin, BPM McCabe, DT Frazier, W Maneesoonthorn, CP Robert Journal of Computational and Graphical Statistics 28 (3), 508-522, 2019 | 37 | 2019 |
| Approximate bayesian forecasting DT Frazier, W Maneesoonthorn, GM Martin, BPM McCabe International Journal of Forecasting 35 (2), 521-539, 2019 | 36 | 2019 |
| Probabilistic forecasts of volatility and its risk premia W Maneesoonthorn, GM Martin, CS Forbes, SD Grose Journal of Econometrics 171 (2), 217-236, 2012 | 22 | 2012 |
| High-frequency jump tests: Which test should we use? W Maneesoonthorn, GM Martin, CS Forbes Journal of econometrics 219 (2), 478-487, 2020 | 21 | 2020 |
| Inversion copulas from nonlinear state space models with an application to inflation forecasting MS Smith, W Maneesoonthorn International Journal of Forecasting 34 (3), 389-407, 2018 | 18 | 2018 |
| Approximate Bayesian computation in state space models GM Martin, BPM McCabe, W Maneesoonthorn, CP Robert arXiv preprint arXiv:1409.8363, 2014 | 18 | 2014 |
| Optimal probabilistic forecasts: When do they work? GM Martin, R Loaiza-Maya, W Maneesoonthorn, DT Frazier, ... International Journal of Forecasting 38 (1), 384-406, 2022 | 10 | 2022 |
| Inversion copulas from nonlinear state space models MS Smith, W Maneesoonthorn arXiv preprint arXiv:1606.05022, 2016 | 5 | 2016 |
| ABC of the future H Pesonen, U Simola, A Köhn‐Luque, H Vuollekoski, X Lai, A Frigessi, ... International Statistical Review, 2022 | 3 | 2022 |
| The predictive ability of quarterly financial statements H Zhou, WO Maneesoonthorn, XB Chen International Journal of Financial Studies 9 (3), 50, 2021 | 1 | 2021 |
| Discussion of ‘Deep learning for finance: deep portfolios’ CS Forbes, W Maneesoonthorn Applied Stochastic Models in Business and Industry 33 (1), 13-15, 2017 | 1 | 2017 |
| Optimal probabilistic forecasts for risk management Y Sun, W Maneesoonthorn, R Loaiza-Maya, GM Martin arXiv preprint arXiv:2303.01651, 2023 | | 2023 |
| Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models W Zhang, MS Smith, W Maneesoonthorn, R Loaiza-Maya arXiv preprint arXiv:2302.13536, 2023 | | 2023 |
| Bayesian Forecasting in the 21st Century: A Modern Review GM Martin, DT Frazier, W Maneesoonthorn, R Loaiza-Maya, F Huber, ... arXiv preprint arXiv:2212.03471, 2022 | | 2022 |
| Approximate Bayesian forecasting (vol 35, pg 521, 2018) DT Frazier, W Maneesoonthorn, GM Martin, BPM McCabe INTERNATIONAL JOURNAL OF FORECASTING 37 (3), 1301-1301, 2021 | | 2021 |
| Inversion copulas from nonlinear state space models with an application to inflation forecasting (vol 34, pg 389, 2018) MS Smith, W Maneesoonthorn INTERNATIONAL JOURNAL OF FORECASTING 37 (3), 1310-1310, 2021 | | 2021 |
| Optimal probabilistic forecasts: When do they work? R Loaiza-Maya, GM Martin, DT Frazier, W Maneesoonthorn, AR Hassan Monash Econometrics and Business Statistics Working Papers, 2020 | | 2020 |
| Supplementary Appendix to “Dynamic Asset Price Jumps: the Performance of High Frequency Tests and Measures, and the Robustness of Inference” W Maneesoonthorn, GM Martin, CS Forbes | | 2018 |
Gael M. MartinProfessor of Econometrics, Department of Econometrics and Business Statistics, Monash UniversityVerified email at monash.edu
