Dr Francesco Ungolo
Lecturer

Dr Francesco Ungolo

Business School
School of Risk and Actuarial Studies
Prior to join The School of Risk and Actuarial Studies at UNSW, Francesco Ungolo earned a Master in Insurance and Risk Management from MIB School of Management discussing a thesis on Longevity Risk modelling under the supervision of Prof. Ermanno Pitacco in January 2015. Meanwhile, he worked for a year as Risk Management consultant where he was part of the model validation team for the calculation of Solvency II capital requirements. In September 2015 he joined Heriot-Watt University (Edinburgh, UK) to pursue the PhD program in Actuarial Mathematics under the supervision of Dr. Torsten Kleinow and Prof. Angus Macdonald, and the collaboration of Dr. Stephen Richards, completed in May 2019. From March 2019 to September 2022 he worked as Postdoctoral Researcher in Statistics at Technology University of Eindhoven (NL, Mar. 2019-Mar.2021), and at Technical University of Munich (GE, March 2021-September 2022). He is currently a qualifying actuary for the Institute and Faculty of Actuaries UK.


His expertise lies in 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 for some lives, or the combined use of different datasets in order to return more robust estimates of mortality rates. Another key research theme is the analysis and 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.

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 | 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

I am currently involved in a project aimed at the development of affine mortality models, their estimation and application 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.

Another key theme of my research is the development of statistical models for the joint analysis of dependent events.