Étienne Marceau
YOU?
Author Swipe
View article: Generalized FGM dependence: geometrical representation and convex bounds on sums
Generalized FGM dependence: geometrical representation and convex bounds on sums Open
Building on the one-to-one relationship between generalized FGM copulas and multivariate Bernoulli distributions, we prove that the class of multivariate distributions with generalized FGM copulas is a convex polytope. Therefore, we find s…
View article: Extremal negative dependence and the strongly Rayleigh property
Extremal negative dependence and the strongly Rayleigh property Open
We provide a geometrical characterization of extremal negative dependence as a convex polytope in the simplex of multidimensional Bernoulli distributions, and we prove that it is an antichain that satisfies some minimality conditions with …
View article: On a risk model with tree-structured Poisson Markov random field frequency, with application to rainfall events
On a risk model with tree-structured Poisson Markov random field frequency, with application to rainfall events Open
In many insurance contexts, dependence between risks of a portfolio may arise from their frequencies. We investigate a dependent risk model in which we assume the vector of count variables to be a tree-structured Markov random field with P…
View article: Centrality and shape-related comparisons in a tree-structured Markov random field
Centrality and shape-related comparisons in a tree-structured Markov random field Open
Understanding the effects of the choice of the tree on the joint distribution of a tree-structured Markov random field (MRF) is crucial for fully exploiting the intelligibility of such probabilistic graphical models. Tools must be develope…
View article: Tree-structured Markov random fields with Poisson marginal distributions
Tree-structured Markov random fields with Poisson marginal distributions Open
A new family of tree-structured Markov random fields for a vector of discrete counting random variables is introduced. According to the characteristics of the family, the marginal distributions of the Markov random fields are all Poisson w…
View article: Generalized FGM dependence: Geometrical representation and convex bounds on sums
Generalized FGM dependence: Geometrical representation and convex bounds on sums Open
Building on the one-to-one relationship between generalized FGM copulas and multivariate Bernoulli distributions, we prove that the class of multivariate distributions with generalized FGM copulas is a convex polytope. Therefore, we find s…
View article: A representation-learning approach for insurance pricing with images
A representation-learning approach for insurance pricing with images Open
Unstructured data are a promising new source of information that insurance companies may use to understand their risk portfolio better and improve the customer experience. However, these novel data sources are difficult to incorporate into…
View article: A representation-learning approach for insurance pricing with images
A representation-learning approach for insurance pricing with images Open
Unstructured data are a promising new source of information that insurance companies may use to understand their risk portfolio better and improve the customer experience. However, these novel data sources are difficult to incorporate into…
View article: Exchangeable FGM copulas
Exchangeable FGM copulas Open
Copulas provide a powerful and flexible tool for modeling the dependence structure of random vectors, and they have many applications in finance, insurance, engineering, hydrology, and other fields. One well-known class of copulas in two d…
View article: A new method to construct high-dimensional copulas with Bernoulli and Coxian-2 distributions
A new method to construct high-dimensional copulas with Bernoulli and Coxian-2 distributions Open
We propose an approach to construct a new family of generalized Farlie-Gumbel-Morgenstern (GFGM) copulas that naturally scales to high dimensions. A GFGM copula can model moderate positive and negative dependence, cover different types of …
View article: Collective risk models with FGM dependence
Collective risk models with FGM dependence Open
We study copula-based collective risk models when the dependence structure is defined by a Farlie-Gumbel-Morgenstern (FGM) copula. By leveraging a one-to-one correspondence between the class of FGM copulas and multivariate symmetric Bernou…
View article: Risk aggregation with FGM copulas
Risk aggregation with FGM copulas Open
We offer a new perspective on risk aggregation with FGM copulas. Along the way, we discover new results and revisit existing ones, providing simpler formulas than one can find in the existing literature. This paper builds on two novel repr…
View article: Efficient evaluation of risk allocations
Efficient evaluation of risk allocations Open
Expectations of marginals conditional on the total risk of a portfolio are crucial in risk-sharing and allocation. However, computing these conditional expectations may be challenging, especially in critical cases where the marginal risks …
View article: Exchangeable FGM copulas
Exchangeable FGM copulas Open
Copulas are a powerful tool to model dependence between the components of a random vector. One well-known class of copulas when working in two dimensions is the Farlie-GumbelMorgenstern (FGM) copula since their simple analytic shape enable…
View article: GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS
GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS Open
Spatial data are a rich source of information for actuarial applications: knowledge of a risk’s location could improve an insurance company’s ratemaking, reserving or risk management processes. Relying on historical geolocated loss data is…
View article: Rethinking Representations in P&C Actuarial Science with Deep Neural Networks
Rethinking Representations in P&C Actuarial Science with Deep Neural Networks Open
Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many emerging data sources (text, images, sensors) may complemen…
View article: Rethinking Representations in P&C Actuarial Science with Deep Neural Networks
Rethinking Representations in P&C Actuarial Science with Deep Neural Networks Open
Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many emerging data sources (text, images, sensors) may complemen…
View article: Mining Actuarial Risk Predictors in Accident Descriptions Using Recurrent Neural Networks
Mining Actuarial Risk Predictors in Accident Descriptions Using Recurrent Neural Networks Open
One crucial task of actuaries is to structure data so that observed events are explained by their inherent risk factors. They are proficient at generalizing important elements to obtain useful forecasts. Although this expertise is benefici…
View article: Machine Learning in P&C Insurance: A Review for Pricing and Reserving
Machine Learning in P&C Insurance: A Review for Pricing and Reserving Open
In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, onl…
View article: Ruin-based risk measures in discrete-time risk models
Ruin-based risk measures in discrete-time risk models Open
View article: Machine Learning in Property and Casualty Insurance: A Review for Pricing and Reserving
Machine Learning in Property and Casualty Insurance: A Review for Pricing and Reserving Open
View article: Tail Approximations for Sums of Dependent Regularly Varying Random Variables Under Archimedean Copula Models
Tail Approximations for Sums of Dependent Regularly Varying Random Variables Under Archimedean Copula Models Open
View article: Collective Risk Models with Hierarchical Archimedean Copulas
Collective Risk Models with Hierarchical Archimedean Copulas Open
View article: A Note on Univariate and Multivariate Mixed Exponential Distributions
A Note on Univariate and Multivariate Mixed Exponential Distributions Open
View article: Tail approximations for sums of dependent regularly varying random\n variables under Archimedean copula models
Tail approximations for sums of dependent regularly varying random\n variables under Archimedean copula models Open
In this paper, we compare two numerical methods for approximating the\nprobability that the sum of dependent regularly varying random variables\nexceeds a high threshold under Archimedean copula models. The first method is\nbased on condit…
View article: Archimedean Copulas: Aggregation, Capital Allocation and Other Applications
Archimedean Copulas: Aggregation, Capital Allocation and Other Applications Open
View article: On a Joint Frequency and Severity Loss Model Applied to Earthquake Risk
On a Joint Frequency and Severity Loss Model Applied to Earthquake Risk Open
View article: On the Impact of Stochastic Volatility, Interest Rates and Mortality on the Hedge Efficiency of GLWB Guarantees
On the Impact of Stochastic Volatility, Interest Rates and Mortality on the Hedge Efficiency of GLWB Guarantees Open
View article: A note on compound renewal risk models with dependence
A note on compound renewal risk models with dependence Open