Deng, L., Smith, M.S. and Maneesoonthorn, W. (2024). Large Skew-t Copula Models and Asymmetric Dependence in  Intraday Equity Returns.  Journal of Business & Economic Statistics, forthcoming.[Link | Code]

Martin, G.M., Frazier, D.T., Maneesoonthorn, W., Loaiza-Maya, R., Huber, F., Koop, G., Maheu, J., Nibbering, D. and Panagiotelis, A. (2024). Bayesian Forecasting in Economics and Finance: A Modern Review. International Journal of Forecasting, 40(2), 811-839. [Link]

Pesonen, H., Simola, U., Köhn-Luque, A., Vuollekoski, H., Lai, X., Frigessi, A., Kaski, S., Frazier, D.T., Maneesoonthorn, W., Martin, G.M. and Corander, J. (2022). ABC of the Future. International Statistical Review, open access.[Link]

Martin, G., Laoiza-Maya, R., Maneesoonthorn, W. , Frazier, D. and Ramirez-Hassan, A. (2021) Optimal Probabilistic Forecasts: When Do They Work? International Journal of Forecasting, 38(1), pp. 384-406. [Link]

Zhou, H., Maneesoonthorn, W. and Chen, X.B. (2021). The Predictive Ability of Quarterly Financial. International Journal of Financial Studies, vol. 9, no. 3, pp. 50. [Link]

Maneesoonthorn, W., Martin, G.M. and Forbes, C.S. (2020). High-Frequency Jump Tests: Which Test Should We Use? Journal of Econometrics, 219(2), 478-487.[Link | Code]

Martin, G.M., McCabe B., Frazier, D., Maneesoonthorn, W. and Robert, C. (2019). Auxiliary Model-Based Approximate Bayesian Computation in State Space Models. Journal of Computational and Graphical Statistics, 28(3), 508-522. [Link|Code]

Frazier, D., Maneesoonthorn, W., Martin, G.M. and McCabe, B. (2018). Approximate Bayesian Forecasting. International Journal of Forecasting, 35(2), 521-539. [Link]

Smith, M.S. and Maneesoonthorn W. (2018). Inversion Copulas from Nonlinear State Space Models with an Application to Inflation Forecasting. International Journal of Forecasting, 34(3), 389-407. [Link | Code] 

Laoiza-Maya, R., Smith, M.S. and Maneesoonthorn W. (2017). Time Series Copulas for Heteroskedastic Data. Journal of Applied Econometrics, 33(3), 332-354. [Link]

Maneesoonthorn, W., Forbes, C. S., and Martin, G. M. (2017). Inference on Self-Exciting Jumps in Prices and Volatility Using High-Frequency Measures. Journal of Applied Econometrics, 32, 504–532. [Link | Data Archive]

Maneesoonthorn, W., Martin, G. M., Forbes, C. S., and Grose, S. D. (2012). Probabilistic Forecasts of Volatility and its Risk Premia. Journal of Econometrics, 171(2), 217-236. [Link]

Forbes, C.S. and Maneesoonthorn, W. (2017). Discussion on Deep Learning in Finance: Deep Portfolios. Applied Stochastic Modelling in Business and Industry, 33, 13–15. [Link]

Maneesoonthorn, W. (2015). High-Frequency Financial Econometrics, by Yacine Ait-Sahalia and Jean Jacod (Princeton University Press, Princeton and Oxford, 2014), pp. xxiv + 659, The Economic Record, 91: 542-544. (Book review). [Link]

Zhang, W., Smith, M.S., Maneesoonthorn, W. and Loaiza-Maya, R. (2023). Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models. [Link]

Sun, Y., Maneesoonthorn W., Loaiza-Maya, R. and Margin G.M. (2023). Optimal Probabilistic Forecasts for Risk Management. [Link]

Probabilistic Predictions of Option Prices Using Multiple Sources of Data. Joint work with Gael Martin and David Frazier (Monash).

Inversion Copulas for Financial Applications. Joint work with Richard Gerlach (Sydney) and Michael Smith (MBS).

Flexible Modelling of Price Jumps with Nonparametric Bayes. Joint with Yuru Sun (MBS) and Yong Song (Melbourne).