My research interests include, but is not limited to, quantitative risk management, dependence uncertainty, sensitivity analysis for insurance, risk measures, and stress testing.
My research is supported by Natural Sciences and Engineering Research Council of Canada (DGECR-2020-00333, RGPIN-2020-04289), the Connaught New Researcher Award.
I am a Principal Investigator of the Collaborative Research Team (CRT) “Natural Catastrophes: Are Canadian Insurers Ready for “The Big One”?” sponsored by CANSSI.
Jaimungal, S. , Pesenti. S.M., Sánchez-Betancourt, L. (2022) Minimal Kullback-Leibler Divergence for Constrained Levy-Ito Processes, available on SSRN/ArXiv.
Pesenti, S.M., Wang, Q., Wang, R. (2021) Optimizing distortion riskmetrics with distributional uncertainty, available on SSRN/ArXiv.
Published /Accepted papers
Pesenti, S. M., Bettini, A., Millossovich, P., Tsanakas A. (2021) Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis, Annals of Actuarial Science, 15(2), 458-483; also available on SSRN/ArXiv.
Pesenti, S. M., Millossovich P. and Tsanakas A., (2019). Reverse sensitivity testing: What does it take to break the model? European Journal of Operational Research, 274(2), pp. 654–670
Pesenti, S. M., Millossovich P. and Tsanakas A., (2018). Euler allocations in the presence of non-linear reinsurance: comment on Major (2018). Insurance, Mathematics and Economics, 83, pp. 29–31.
Pesenti, S. M., Millossovich P. and Tsanakas A., (2016). Robustness regions for measures of risk aggregation. Dependence Modeling, 4(1), pp. 348–367.
Pesenti, S. M., Bettini, A., Millossovich, P., Tsanakas A. (2022). SWIM: Scenario Weights for Importance Measurement. R package version 1.0.0.