Background The estimation of archimedean copulas is an issue that bothers me since… a while now. My first shot at reasearch, while still being a master’s student, was a project with a few friends involving those models into a solvency 2 and reinsurence pricing problem.
Part 1: About the Gumbel-Barnett generator and its derivatives. In this article, I discuss an interesting problem encountered while working on Copulas.jl, a Julia package for copula routines that I have developed and continue to maintain.
A few months ago, I was proposed the opportunity to give a talk at ISBA’s Young researcher’s day 2023 (UcLouvain, Louvain-la-neuve, BE) about version control. I already wrote a bit on git and GitHub for latex writing and even more automation.
Announce I am proud to annonce the publication and the registration of my new Julia package, Copulas.jl.
As it’s name suggests, Copulas.jl is a package that implements methods and tools to work with an arround copulas in the Julia programming language.
The problematic A latex-written resume is always a nice thing to have: easy to update, practical to integrate .bib bibliographies, and automatic management of the look (you only provide its content).
The problematic As an academic, I spend my life writing papers. Since these papers are mostly about math or some applications of math, I am an extensive user of latex.
The cort package is now on cran ! The cort package provides S4 classes and methods to fit several copula models:
The classic empirical checkerboard copula and the empirical checkerboard copula with known margins, see Cuberos, Masiello and Maume-Deschamps (2019) https://arxiv.
After working on a bootstrapping framework for the Mack model, with a one-year point of view and with several triangles to bootstrap jointly, i decided to put some of my code into a litle package, mbmcl.
My actuarial thesis got published online there
This work took me a little more than one year to do, an was dealing with non-life reserving in solvency 2 context for the french decenial insurance contracts.
Introduction Suppose you have a dataset, and you are narowing possible machine learning models to 2 or 3 models, but you still cant choose which you want : Will the benefit of understandability from my CART cost me too much compare to a random forest or some bootsting ?