Current Research Projects



FW-B (Federation Wallonie-Bruxelles) 2020-2025

Imperfect Data : From Mathematical Foundations to Applications in Life Sciences (IMAL)

We are witnessing a period of time where the data collection potential has increased exponentially. The cautionary tale of this big data era is that large amounts of data do not necessarily contribute to an increment in our knowledge about the underlying phenomenon. One of the principal reasons for this is that even though one would desire to measure a characteristic for a subject, in many instances one can only get an approximate measurement due to difficulty in obtaining the direct measurement of the desired phenomenon (e.g. tumor size), non-replicability across instances (e.g. blood pressure), necessity to obtain numerous measurements rapidly, sometimes at the cost of accuracy. As a result, many modern observed markers are proxies for the real data because invasive, costly or too complex methods would be required to obtain accurate measurements. In this project we study how one can correct for different types of imperfect data when building statistical models with a focus on applications coming from life sciences. Imperfect data appear in different contexts, structures and models, and this project focuses on two common settings which regularly suffer from imperfect data: data in a regression context with imperfectly measured explanatory variables (Theme 1) and highdimensional or functional data with measurement error (Theme 2).

Promoters : Catherine Legrand (porte-parole, UCLouvain), Anouar El Ghouch (UCLouvain), Philippe Lambert (UCLouvain / ULiège),
Eugen Pircalabelu (UCLouvain), Germain Van Bever (UNamur), Ingrid Van Keilegom (UCLouvain / KU Leuven).
ARC project

FW-B (Federation Wallonie-Bruxelles) 2018-2023

Sustainable, Adequate and Safe Pensions

This interdisciplinary research project (law, economics, actuarial science, philosophy) aims at critically assessing the key conditions that a public pension system should fulfil to be successfully reformed. Our hypothesis is that there are three such conditions: i) financial sustainability, ii) social adequacy and iii) safe governance. Hence, the ‘SAS’ acronym. Our goal is to identify the pension architecture that is the most likely to generate SAS pensions.

Promoters : Pierre Devolder, Alexia Autenne, Jean Hindriks, Vincent Vandenberghe, Axel Gosseries (ARC project)
Website :


FW-B (Federation Wallonie-Bruxelles) 2018-2023

Negative and ultra-low interest rates: behavioral and quantitative modelling

Interest rates are a cornerstone of economics and finance. They are at the foundation of asset pricing and monetary policy, and more generally of all intertemporal choices made by market participants and institutions every day, with huge consequences for the economic activity and wellbeing of our societies. Until recently, it was assumed (mostly implicitly) that interest rates could only possibly be positive. Notwithstanding, in the wake of the financial crisis initiated in 2008, major central banks of developed countries have been brought to conduct rates policies that turned them negative. The consequences of such a paradigm shift are both potentially huge and not well understood yet. This research project aims at shedding light on these consequences, both from an academic and a policy viewpoint, following three intertwined research lines that bring together a multidisciplinary team of researchers working on Behavioral Finance, Macro Finance, and Quantitative Finance.

Promoters: Catherine D’Hondt, Julio Dávila, Leonardo Iania, Christian Hafner, Olivier Corneille and Frederic Vrins.
ARC project

RW (Région wallonne) 2018-2021


The BeNeFit project is a collaborative effort between UCLouvain (Main promotor: Catherine Legrand, ISBA-LIDAM), two private industry (IDDI, BMS), one non-profit organization (EORTC, via an Innoviris funding) and the Hopitaux Universitaires de Lyon.
This project, entitled "Biostatistical Estimation of Net Effects for Individualization of Therapy” obtained a Biowin grant (Pôle de compétitivité Région Wallone). The objective is to develop a new method to combine information about the different aspects of a new therapy (short and long term outcome, toxicity, quality of life) rather than focusing on an primary endoint as is currently done in clinical research. This new statistical methodology, based on further developments of “generalized pairwise comparisons”, will also be implemented in an appropriate software to allow the analysis of the data from a clinical trial while choosing a hierarchy in these outcomes, based on the priority of each patient and medical doctor to provide a specific answer in terms of risk-benefit.

Promoter : Catherine Legrand

SAS Partnership 2018-2022


The SAS software is one of the most used statistical software in the world. Since several years, there exist a partenariat between SAS and Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA) through which courses of programming in SAS and data mining techniques are organized. These courses are open to all master students as well as to PhD students and to all researchers of the UCLouvain. Within the context of this partenariat, SAS also support (financially and logistically) the organisation of short courses within ISBA.

Promoter: Catherine Legrand

BSP (Belgian Science Policy) 2016-2021

BRAIN-be : Valorisation de 70 ans d'observations soclaires de l'Observatoire Royal de Belgique

This is an interdisciplinary project (VAL-U-SUN) between the Solar Influence Data Analysis Center of the Royal Observatory Belgium (Uccle, Brussels), Drs Laure Lefevre et Veronique Delouille, and l’ISBA, on the analysis of the international Sunspot Index. In a four years’ PhD project, the task is to statistically characterise the sunpot data set, by developing a model for its short and long term behaviour over time, including its statistical uncertainties. The overall goal is to come up with an automated quality control algorithm that allows (almost) online supervision of the evolution of the data reported by a collection of sunspot observation station around the world that contribute to establishing a statistically “clean” index. Assistance to this project is given by the SMCS (Christian Ritter).

Promoter: Rainer von Sachs, Researcher: Sophie Mathieu

FNRS CDR 2019-2020

Modeling of jump clustering for managing interest rates and creditrisks

Our research focuses on the modelling of jump clustering for interest rates and default risks with Hawkes processes. A team of two PhD students and me explores two axis. The first axis focuses on the modelling of jump clustering in interest rates. Our main purpose is to explain the spread between swap rates of same maturity but of different tenors, arising from the risk of liquidity breakdowns in the interbank market. The second axis focuses on the actuarial evaluation of credit risk. Insurance companies propose a wide variety of credit coverages e.g. trade credit insurance, business credit insurance, export credit insurance. Most of stakeholders in these contracts are non-listed small or medium companies and there is no alternative to diversification for hedging the insurer’s exposure to default risk. We will focus on the valuation of these contracts with actuarial techniques when the intensity of default is a jump diffusion process with and without jump clustering. Both axis are related through the fact that we aim to model events of bankruptcy. For axis 1, we study the bankruptcy of the interbank market while
we analyse the default risk of a firm in the second project.

Promoter: Donatien Hainaut

FNRS CDR 2019-2020

Optimal transport in nonparametric statics: Copulas for non-Euclidean data and multivariate tail quantile countours

The aim is to explore the potential of concepts and methods from the theory of optimal measure transport for statistical modelling and inference. We will extend the classical probability integral and quantile transforms on the real line to more general state spaces thanks to cost-minimizing mappings pushing a reference measure towards a target measure. Based on these transforms, we will seek to extend Sklar's celebrated copula theorem to more general metric spaces. The unit circle will serve as test case, and will allow us to model dependence between directional data. Further, we will study properties of tail quantile contours of multivariate regularly varying distributions defined via cyclically monotone mappings. Such contours are expected to satisfy a shape constraint involving a compact convex body, and we will look to exploit this information to construct efficient estimators.

Promoter: Johan Segers

FNRS PDR 2016-2020

Semiparametric inference for multi-state models

Promoter: Anouar El Ghouch, Researcher: Kassu Mehari Beyene

FNRS PDR 2016-2020

Risk management and pricing in finance and insurance

In this project we will develop new risk management and pricing tools for several risks in finance and insurance.
A) A first goal of this research is to develop a multiple curve interest rate model that combines tractable model dynamics and
semi-analytic pricing formulae with positive interest rates based on positive multiplicative spreads and by including regime switching.
In this model we will first price interest rate derivatives. Afterwards we will study the influence of future cash flow evaluation by multiple curve models for
the valuation and solvency requirements of life insurance and pension liabilities.
B) A second aim is to obtain a multivariate stochastic skew model by introducing first a tractable new time-changed Lévy model and afterwards its multivariate extension.
We will focus on Index1Equity options markets as well as on foreign exchange markets. We will derive fast and accurate pricing formulae for Vanilla options through the
use of FFT techniques. All the qualities of both the univariate and multivariate model will be illustrated by several numerical examples first mainly in
derivatives pricing but later also in solvency risk measurement for a bank or insurance portfolio.
C) A third goal concerns the valuation and solvency requirements of life insurance and pension liabilities. Since the Solvency II regulation is based on
a one year time horizon for the computation of the solvency capital we will first focus upon the long term aspect of the life insurance and pension liabilities.
Therefore we will develop alternative risk measurements to Solvency II based on a time consistent framework inspired by dynamic risk measures and iteration techniques.
In terms of valuation taking into account the joined presence of actuarial and financial risks we will develop a universal pricing method coherent with the
risk neutral pricing in finance but also in line with the law of large numbers and the classical premium principles used in insurance.

Promoter: Pierre Devolder

Partnership KULeuven - UCLouvain

Quantile regression for censored data

One of the statistical challenges in survival analysis is the study of the relationship between a time-to-event response T and a set covariates X. This can be done using a wide variety of regression techniques like, for example, linear, AFT or Cox models. A robust and flexible alternative to these classical models is quantile regression, which has gained considerable popularity and interest in recent years. Many methods have been developed for quantile regression with completely observed data. But when data are subject to censoring, statistical estimation and inference become more difficult, and the literature is sparse. The existing work focuses on the case of i.i.d. data with a right-censored response, but in practice censoring mechanisms can be quite complicated (e.g. interval censoring) and may concern both the response and the covariates. The objective of this project is to develop and study consistent and computationally efficient procedures to conduct estimation and inference in quantile regression models with complicated censoring mechanisms. To this end, an enriched asymmetric Laplace distribution will be proposed and studied. Once studied, this distribution will be used to investigate the case of quantile regression with (1) censored response, (2) censored covariates and (3) censored response and censored covariates.

Promoter: Anouar El Ghouch & Ingrid Van Keilegom


ETHIAS Chair 2019-2022

Fully funded Pension Systems

The purpose of this interdisciplinary research project (law, actuarial science) is to look at the future of fully funded occupational pension schemes in the context of ageing and low interest rates.  

Promoter: Pierre Devolder

Generali Chair 2016-2020

Longevity Risk and Pension

The purpose of the research is to develop actuarial and financial models in order to estimate the impact of various scenarios of reform of the pension systems in Belgium.
In particular, stochastic models will be considered in order to establish actuarial balance sheets of the social security pension system.  
The longevity risk in particular will be deeply analyzed and modeled.

Promoter: Pierre Devolder

AG Insurance Chair 2016-2020

Pension valuation and solvency

Development of a coherent and universal model of valuation and solvency requirement of pension liabilities for pension funds and insurance companies in a stochastic environment.

Promoter: Pierre Devolder

AXA Research Fund 2016-2020

Actuarial dynamic approach of customer in P&C

While actuaries carry their models’ calculations for insurance products considering each product in isolation, the consumers tend to
view all the products bought in a global way. This research project conducted with AXA Belgium aims at reconciling the two points of view, allowing insurers to offer the most appropriate damage insurance covers together with optimal premiums.

Promoter: Michel Denuit