“La pensΓ©e n’est qu’un Γ©clair au milieu d’une longue nuit. Mais c’est cet Γ©clair qui est tout.”
Thought is only a flash in the middle of a long night. But this flash means everything.
– Henri PoincarΓ© (1854-1912), La valeur de la science.
π’ PhD Position in Actuarial Science: Climate risks in Life Insurance
We are offering a full-time PhD position at ISBA (UCLouvain) starting in September 2025.
The project focuses on the integration of climate risks into mortality modeling, financial risk management, and the development of innovative life insurance products.
π Application deadline: May 25th, 2025
Karim Barigou
Since September 2024, I am Professor of Actuarial Sciences at the Louvain School of Management (LSM) and the Louvain Institute for Multidisciplinary Analysis and Quantitative Modeling (LIDAM) at UniversitΓ© catholique de Louvain (UCLouvain).
My research interests are in life insurance and its interplay with quantitative finance. My recent work covers diverse areas such as mortality modelling with pandemics and climate effects, longevity risk management, pricing and hedging variable annuities, and the application of statistical learning techniques in life insurance.
Previously, I was Professor in the School of Actuarial Science at UniversitΓ© Laval (Quebec, Canada). I also worked as a postdoctoral researcher at ISFA, University Lyon 1 (2019-2022), focusing on longevity risk online detection and model selection. I earned my Ph.D. in Business Economics from KULeuven in 2019, where my research under the supervision of Jan Dhaene focused on combining market-consistent and actuarial valuation methods for life insurance liabilities.
Open-Source SofTware
StanMoMo: An R package for Bayesian Mortality Modelling with Stan
Glad to share a new R package for Bayesian Mortality Modelling. The package provides functions to fit and forecast standard mortality models (Lee-Carter, APC, CBD, etc) in a full Bayesian setting. The package also includes functions for model selection and model averaging based on leave-future-out validation.
Most Recent Papers
Granular mortality modeling with temperature and epidemic shocks: a three-state regime-switching approach
How do temperature extremes and epidemics impact mortality beyond seasonal trends? This study introduces a novel regime-switching framework to model deviations in mortality driven by environmental and respiratory shocks. Our model identifies three distinct states: a baseline state capturing regular seasonal patterns, an environmental shock state reflecting excess mortality during heatwaves, and a respiratory shock state addressing spikes caused by influenza and COVID-19 outbreaks. Using weekly mortality data from 21 French regions across six age groups, we model state transitions with dynamic predictors, including lagged temperature and influenza incidence rates. Through scenario-based projections and bootstrap uncertainty quantification, this framework provides valuable insights for healthcare planning and risk management, helping hospitals and policymakers prepare for future mortality shocks.
Derivatives under market impact: Disentangling cost and information
This paper explores the challenges faced by large traders when pricing and hedging derivatives, particularly the risks of market manipulation. We introduce a framework inspired by insider trading to understand how a large traderβs knowledge and actions affect market dynamics. Within this setup, we identify special probability measures that simplify pricing for an informed trader. We then propose a hedging strategy that balances transaction costs, market impact, and manipulation risks. Numerical results illustrate how this strategy performs for an out-of-the-money call option compared to the Black-Scholes model.
Expected Utility Optimization with Convolutional Stochastically Ordered Returns
This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivatives of expected utility for various utility functions, significantly broadening the potential for analytical and computational applications. We apply these theoretical advancements to a case study involving the optimal production strategies of competitive firms, demonstrating the practical implications of our findings in economic decision making.
Bayesian mortality modelling with pandemics: a vanishing jump approach
We propose novel single- and multi-population mortality models with vanishing jumps, where the impact of shocks is strongest initially and gradually fades over time. Existing models typically assume either transitory one-period jumps or permanent shifts. Using data from COVID-19 and the World Wars, our Bayesian approach outperforms transitory shock models in both in-sample and out-of-sample tests, offering a more realistic representation of mortality dynamics during pandemics.
Actuarial-consistency and two-step actuarial valuations: a new paradigm to insurance valuation
Motivated by solvency regulations, the recent focus has been towards financial risks and the so-called market-consistent valuations. In this situation, the financial market is the main driver and actuarial risks only appear as the βsecond stepβ. This paper goes against the tide and introduces the concept of πππππππππ-ππππππππππ valuations where actuarial risks are at the core of the valuation. We propose a πππ-ππππ πππππππππ πππππππππ that is first driven by actuarial information and is actuarial-consistent. We also provide a detailed comparison between actuarial-consistent and market-consistent valuations
Bayesian model averaging for mortality forecasting using leave-future-out validation
Relying on one specific model can be too restrictive and lead to some well documented drawbacks including model misspecification, parameter uncertainty and overfitting. In this paper, we consider a Bayesian Negative-Binomial model to account for parameter uncertainty and overdispersion. Moreover, model averaging based on out-of-sample is considered as a response to model misspecifications and overfitting. Overall, we found that our approach outperforms the standard Bayesian model averaging in terms of prediction performance and robustness.
Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect
Most mortality models are very sensitive to the sample size or perturbations in the data. In this paper, we show how regularization and cross-validation can be used to smooth and forecast the mortality surface. In particular, our approach outperforms the P-spline model in terms of prediction and is much more robust when including Covid-type effect.
Insurance valuation: A two-step generalized regression approach
There is still an open debate on how to appropriately define a "fair" value and a risk margin for long-term insurance liabilities. In this paper, we discuss how solvency constraints can be efficiently included in the hedging process and the risk margin.
Pricing equity-linked insurance by neural networks
In this paper, we price a portfolio of equity-linked contracts in a general incomplete actuarial-financial market. We show that the pricing problem can be efficiently handled by the use of neural networks.
