Hi All,

Now that it’s all over and we can freely share our ideas I’m curious if anyone tried something based on non-homogeneous regression or quantile regression.

I seen that the WorldExperts team did fit a “zero inflated log normal” which to my understanding modelled a conditional variance parameter. My idea would be to use that uncertainty information in the pricing part of the process

Our team briefly tried quantile regression on the severity component. I think the model was something like frequency * 95% quantile of severity. Both the quantile model and frequency model fit by light gradient boosting

Which we then ensembled with a classicial pricing strategy of 1.15*E(claim), taking the maximum of both strategies. This gave us a very small market share and profit for week 8 and 9.

Sadly I didn’t have much time to play around with it but I feel like it might be fruitful!

If I did have time I was planning on fitting some conditional distribution, for example maybe with https://www.gamlss.com/ OR neural networks, the output layer being parameters for a distribution. Then try pricing strategies along the lines of

E(claim)+multiplier*standard deviation(claim)

I feel like this might be a nice single model way to deal with the potentially large claims.

Anyone else play around with ideas like this? I thought it would be quite a natural way to approach it.