:::::: :::

::: :::

::: ::::::

`CMP; SD=0.111 (0.0661), cor=0.915 ar fit, r2=0.268, rse0=0.089`

- Line; standard deviation
- Down-side semi-deviation is shown in parenthsis (explain)
- Correlation of the line with total on the first line
- (second line) shows the \(R^2\) and residual standard error of an autoregressive loss ratio model

- When the
`rse`

is much lower than`SD`

it suggests the market cycle is predictable - Tends to occur in casualty lines (e.g., commercial auto, medical malpractice, private passenger auto, and workers compensation)
- The cycle for property lines tends to be idiosyncratic, for obvious reasons.

- Thin gray line in each plot shows the
`total`

loss ratio, for context - The horizontal lines show the mean (thicker) and mean \(\pm \Phi^{-1}(22/23)= \pm1.71\) standard deviations
- If the loss ratios were normally distributed we expect all observations from 22 years (1996-2017) to fall within these tram lines
- They provide a surprisingly good estimate of the range of loss ratio, except for
`Financial Lines`

(which uses a different tick spacing, note).

posted 2021-11-30 | tags: insurance, risk, pricing, presentations

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