Ole Maneesoonthorn

Ole Maneesoonthorn is an Associate Professor of Statistics and Econometrics, and currently serves as Associate Dean, Research at the Melbourne Business School, the University of Melbourne, Australia. Her research are in the fields of time series econometrics, financial econometrics and Bayesian computation. Ole has published in top field journals in econometrics, such as the Journal of Econometrics and Journal of Applied Econometrics. She has been recognized on many occasions for her research and presentation skills. These include winning the prize for best PhD paper at both the inaugural Peter C.B. Phillip PhD Camp in 2012 (held at the National University of Singapore) and the 2010 Financial Integrated Research Network (FIRN) Doctoral Tutorial; and an honourable mention at the 2013 New Zealand Econometrics Study Group.

Ole received a PhD in Econometrics from Monash University. The work on her thesis earned her the prestigious International Savage Award, bestowed by the International Society of Bayesian Analysis (ISBA) for the most outstanding doctoral dissertations in Bayesian econometrics or statistics, as well as the Mollie Holman Doctoral Medal 2013 from Monash University.

Ole's teaching specialty includes Data Analysis for the Master of Business Administration; and Statistics Accelerator, Statistics Learning, Predictive Analytics and Finance Analytics for the Master of Business Analytics.

Recent Publications

• 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.

• 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.

• 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.

• 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.

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

• 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.

Notable Grants and Awards

• ARC Discovery Project, 2020-2022. Project title: “Loss-based Bayesian Prediction”.
• Teaching Excellence Award for outstanding teaching in the Master of Business Analytics, 2022 and 2016.
• 2022 Melbourne Business School Dean’s Academic Excellence Award in Teaching, Melbourne Business School.
• Certificate for Research Excellence for 2017, Faculty of Business and Economics, University of Melbourne.
• The Savage Award 2013 for excellence in a PhD thesis in Bayesian statistics in the Applied Methodology category.
• The Mollie Holman Doctoral Medal 2013 for excellence in PhD thesis from Monash University.