I am currently a Post-Doctoral researcher at UCLouvain, in Belgium, under an FNRS grant. Actuary by formation, I focused during my PhD on high dimensional statistics and dependence structure estimations applied to internal modeling in a reinsurance context. I do have a taste for numerical code and open-source software, and most of my work is freely available on Github.
We expose a problem of estimation of multivariate convolutions of gamma random variables, which has very bad numerical properties. This bad numerical behavior literally forced us to use Julia. We describe why Python, R or C++ were not capable of solving our problem and argue that the multiple dispatch paradigm in Julia was the reason we were able to reuse existing code.
Copulas.jl package brings standard dependence modeling routines to native Julia. Copulas are distributions functions on the unit hypercube that are widely used (from theoretical probabilities and Bayesian statistics, to applied finance or actuarial sciences) to model the dependence structure of random vectors apart from their marginals. This native implementation leverages the
Distributions.jl framework and is therefore conveniently directly compatible with the broader ecosystem.