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Dr Francesco Ungolo

Dr Francesco Ungolo

Lecturer
Business School
School of Risk and Actuarial Studies
Francesco Ungolo is a Lecturer in the School of Risk and Actuarial Studies of the UNSW Business School, and an Associate Investigator at the ARC Centre of Excellence in Population Ageing Research (CEPAR). Francesco completed his doctoral studies in Actuarial Mathematics at Heriot-Watt University (Edinburgh, UK, 2019) with a thesis on the statistical analysis of actuarial data with missing observations under the supervision of Dr. Torsten Kleinow and Prof. Angus Macdonald, and the collaboration of Dr. Stephen Richards. He also worked as postdoctoral researcher in the section of Statistics of Technische Universiteit Eindhoven (Eindhoven, the Netherlands, 2019-2021) and at the Chair of Mathematical Finance of the Technische Universität München (Munich, Germany, 2021-2022). He is currently a qualifying actuary for the Institute and Faculty of Actuaries UK
E-mail
f.ungolo@unsw.edu.au
Location
UNSW Business School, East Lobby, Lev. 5
  • Book Chapters | 2021
    Ungolo F; Kleinow T; Macdonald AS, 2021, 'Parametric Bootstrap Estimation of Standard Errors in Survival Models When Covariates are Missing', in Mathematical and Statistical Methods for Actuarial Sciences and Finance eMAF2020, Springer, pp. 389 - 394, http://dx.doi.org/10.1007/978-3-030-78965-7
  • Journal articles | 2024
    Euthum M; Scherer M; Ungolo F, 2024, 'A neural network approach for the mortality analysis of multiple populations: a case study on data of the Italian population', European Actuarial Journal, 14, pp. 495 - 524, http://dx.doi.org/10.1007/s13385-024-00377-5
    Journal articles | 2024
    Ungolo F; Garces LPDM; Sherris M; Zhou Y, 2024, 'AffineMortality: An R package for estimation, analysis, and projection of affine mortality models', Annals of Actuarial Science, http://dx.doi.org/10.1017/S1748499524000149
    Journal articles | 2024
    Ungolo F; Garces LPDM; Sherris M; Zhou Y, 2024, 'Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models', North American Actuarial Journal, 28, pp. 570 - 592, http://dx.doi.org/10.1080/10920277.2023.2238793
    Journal articles | 2024
    Ungolo F; van den Heuvel ER, 2024, 'A Dirichlet process mixture regression model for the analysis of competing risk events', Insurance: Mathematics and Economics, 116, pp. 95 - 113, http://dx.doi.org/10.1016/j.insmatheco.2024.02.004
    Journal articles | 2022
    Ungolo F; van den Heuvel ER, 2022, 'Inference on latent factor models for informative censoring', Statistical Methods in Medical Research, 31, pp. 801 - 820, http://dx.doi.org/10.1177/09622802211057290
    Journal articles | 2020
    Ungolo F; Kleinow T; Macdonald AS, 2020, 'A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates', Insurance: Mathematics and Economics, 91, pp. 68 - 84, http://dx.doi.org/10.1016/j.insmatheco.2020.01.003
    Journal articles | 2019
    Ungolo F; Christiansen MC; Kleinow T; MacDonald AS, 2019, 'Survival analysis of pension scheme mortality when data are missing', Scandinavian Actuarial Journal, 2019, pp. 523 - 547, http://dx.doi.org/10.1080/03461238.2019.1580610

Francesco's research interests include the analysis and development of statistical models for the analysis of complex actuarial datasets involving, among other things, cases of corrupted data, such as missing data, censoring, truncation and the treatment of protected features. Another key research theme is the development of stochastic mortality models for the analysis of single and multiple populations, with a closer, albeit nonexclusive, focus on continuous time affine mortality models. The particular application lies within the analysis of individual savings and retirement decision making with emphasis on the development of innovative product solutions using LTC, health, annuities and life insurance.