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
My Research Activities
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.
My Research Supervision
Supervision keywords
Areas of supervision
- Stochastic mortality modelling
- Annuity portfolio hedging strategies
- Mixture models
- Statistical methods for the analysis of actuarial datasets:
- Address the problem of missing data
- Joint modelling of dependent outcomes, e.g. husband-wife lifetimes, frequency-severity of non-life insurance claims, competing risks, multiple decrement tables
- Dealing with the impossibility of using protected individual characteristics (e.g. gender, ethnicity, medical history) in pricing insurance policies
- Bayesian methods for the analysis of large dimensional datasets (e.g. using telematics data)
- Computational methods for asset-liability management of insurance companies
- Causal methods for policyholder decision making
https://orcid.org/0000-0002-6642-0425