Modelling Extremal Events - Cramer-Lundberg theorem under LogN

I’ve had the textbook 'Modelling Extremal Events: For Insurance and Finance’ sat on my shelf for a while, and last week I finally got around to working through a couple of chapters. One thing I found interesting, just around how my own approach has developed over the years, is that even though it’s quite a maths heavy book my instinct was to immediately build some toy models and play around with the results. I recall earlier in my career, when I had just got out of a 4-year maths course, I was much more inclined to understand new topics via working through proofs step-by-step in long hand, pen to paper. 

In case it’s of interest to others, I thought I’d upload my Excel version I built of the classic ruin process. In particular I was interested in how the Cramer-Lundberg theorem fails for sub-exponential distributions (which includes the very common  Lognormal distribution). Therefore the Spreadsheet contains a comparison of this theorem against the correct answer, derived from monte carlo simulation.

The Speadsheet can be found here:​
https://github.com/Lewis-Walsh/RuinTheoryModel

The first tab uses an exponential distribution, and the second uses a Lognormal distribution. Screenshot below.

​​I also coded a similar model in Python via Jupyter Notebook, which you can read about below.

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