## kernels.cdr_risk_measures()


VaR and TVaR of the one-year LOSS, from simulated CDR draws.


Usage

``` python
kernels.cdr_risk_measures(
    pred,
    levels=(0.995,),
)
```


The capital question is asked on the adverse side, so everything here is stated on the loss `-CDR` (the reserve strengthening): `VaR_0.995` is the 99.5th percentile of that loss, the Solvency II reserve-risk basis, and `TVaR_0.995` its mean beyond that point. A negative VaR means even the adverse tail at that level is still a release.

Quantiles are exact empirical order statistics of the draws, so the tail knots are as good as the draw count and no better - at 20k draws the 99.5th percentile rests on 100 observations. Raise [n_draws](gallery.CohortForecast.md#ibnr.gallery.CohortForecast.n_draws) before reading much into 99.9.

Needs the simulated distribution rather than the analytic msep: a closed form gives a second moment, and no second moment implies a quantile.