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.

Submitted papers

Miao, K. E. and Pesenti, S.M. (2024) Robust Elicitable Functionals, available at SSRN/ArXiv

Jaimungal, S., and Pesenti, S.M. (2024) Kullback-Leibler Barycentre of Stochastic Processes, available at SSRN/ArXiv

Pesenti, S. M., Millossovich P. and Tsanakas A. (2023) Differential Sensitivity in Discontinuous Models, available at SSRN/ArXiv

Kroell, E., Jaimungal S., Pesenti, S. M.,(2023) Optimal Robust Reinsurance with Multiple Insurers, available at SSRN/ArXiv

Published /Accepted papers

Jaimungal, S. , Pesenti, S.M., Saporito, Y., Targino, R., (2024) Risk Budgeting Allocation for Dynamic Risk Measures, Operations Research (forthcoming), available at SSRN/ArXiv.

Pesenti, S. M., Vanduffel, S., (2024) Optimal Transport Divergences induced by Scoring Functions, Operations Research Letters 57:107146, available at SSRN / ArXiv.

Pesenti, S.M., Wang, Q., Wang, R. (2024) Optimizing distortion riskmetrics with distributional uncertainty, Mathematical Programming (forthcoming), available on SSRN/ArXiv.

Moresco, M., Mailhot, M., Pesenti S.M., (2024) Uncertainty Propagation and Dynamic Robust Risk Measures, Mathematics of Operations Research (forthcoming), available at SSRN/ArXiv.

Jaimungal, S. , Pesenti. S.M., Sánchez-Betancourt, L. (2024) Minimal Kullback-Leibler Divergence for Constrained Levy-Ito Processes, SIAM J. Control and Optimization 62(2), pp. 982-1005, available on SSRN/ArXiv.

Kroell, E., Pesenti, S. M., Jaimungal S., (2023) Stressing Dynamic Loss Models, Insurance, Mathematics and Economics, 114, pp. 56-78, available at SSRN/ArXiv.

Bernard, C., Pesenti S., Vanduffel, S. (2023) Robust Distortion Risk Measures, Mathematical Finance 34(3),pp. 774-818, available on SSRN/ArXiv.

Pesenti, S.M and Jaimungal S., (2023) S. Portfolio Optimisation within a Wasserstein Ball, SIAM J. Financial Mathematics, 14(4), pp. 1175-1214, available on SSRN /ArXiv.

da Costa, B. F. P., Pesenti, S. M., Targino, R., (2023) Risk Budgeting Portfolios from Simulation, European Journal of Operational Research, 3(311), pp. 1040-1056, available on SSRN/ArXiv.

Fissler, T., Pesenti, S. M., (2023) Sensitivity Measures Based on Scoring Functions, European Journal of Operational Research 307 (3), 1408-1423, available on SSRN/ArXiv.

Pesenti, S.M., (2022) Reverse Sensitivity Analysis for Risk Modelling, Risks, 10.7, 141, available on SSRN/ArXiv.

Ince, A., Peri, I., Pesenti, S.M., (2022) Risk Contributions of Lambda Quantiles, Quantitative Finance 22:10, 1871-1891, available on SSRN/ArXiv.

Jaimungal, S., Pesenti, S. M., Wang, Y. S., Tatsat, H., (2021) Robust Risk-Aware Reinforcement Learning, SIAM J. Financial Mathematics, 13(1), pp.213-226; also available on SSRN/ArXiv.

Pesenti, S. M., Millossovich P. and Tsanakas A. (2021) Cascade Sensitivity Measures , Risk Analysis, 41(12), pp. 2392-2414; also available on SSRN.

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.

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; also available on SSRN.

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; also available on SSRN.

Pesenti, S. M., Millossovich P. and Tsanakas A., (2016). Robustness regions for measures of risk aggregation. Dependence Modeling, 4(1), pp. 348-367, also available on SSRN.

Software

Pesenti, S. M., Bettini, A., Millossovich, P., Tsanakas A. (2022). SWIM: Scenario Weights for Importance Measurement. R package version 1.0.0.