# tailloss

Evaluate the probability in the upper tail of the aggregate loss
distribution using different methods: Panjer recursion, Monte Carlo
simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff
bound.

`tailloss`

contains functions to estimate the exceedance
probability curve of the aggregated losses. There are two ‘exact’
approaches: Panjer recursion and Monte Carlo simulations, and four
approaches producing upper bounds: the Markov bound, the Cantelli bound,
the Moment bound, and the Chernoff bound. The upper bounds are useful
and effective when the number of events in the catalogue is large, and
there is interest in estimating the exceedance probabilities of
exceptionally high losses.

### References

- Gollini, I., and Rougier, J. C. (2015), “Rapidly bounding the
exceedance probabilities of high aggregate losses”, arXiv:1507.01853.