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Worapree (Ole) Maneesoonthorn
Worapree (Ole) Maneesoonthorn
Associate Professor, Department of Econometrics and Business Statistics, Monash University
Verified email at monash.edu - Homepage
Title
Cited by
Cited by
Year
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
402017
Time series copulas for heteroskedastic data
R Loaiza‐Maya, MS Smith, W Maneesoonthorn
Journal of Applied Econometrics 33 (3), 332-354, 2018
382018
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
372019
Approximate bayesian forecasting
DT Frazier, W Maneesoonthorn, GM Martin, BPM McCabe
International Journal of Forecasting 35 (2), 521-539, 2019
362019
Probabilistic forecasts of volatility and its risk premia
W Maneesoonthorn, GM Martin, CS Forbes, SD Grose
Journal of Econometrics 171 (2), 217-236, 2012
222012
High-frequency jump tests: Which test should we use?
W Maneesoonthorn, GM Martin, CS Forbes
Journal of econometrics 219 (2), 478-487, 2020
212020
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
182018
Approximate Bayesian computation in state space models
GM Martin, BPM McCabe, W Maneesoonthorn, CP Robert
arXiv preprint arXiv:1409.8363, 2014
182014
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
102022
Inversion copulas from nonlinear state space models
MS Smith, W Maneesoonthorn
arXiv preprint arXiv:1606.05022, 2016
52016
ABC of the future
H Pesonen, U Simola, A K÷hn‐Luque, H Vuollekoski, X Lai, A Frigessi, ...
International Statistical Review, 2022
32022
The predictive ability of quarterly financial statements
H Zhou, WO Maneesoonthorn, XB Chen
International Journal of Financial Studies 9 (3), 50, 2021
12021
Discussion of ‘Deep learning for finance: deep portfolios’
CS Forbes, W Maneesoonthorn
Applied Stochastic Models in Business and Industry 33 (1), 13-15, 2017
12017
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
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