reserving.ExpectedLoss

The expected loss ratio method: each origin’s ultimate is apriori

Usage

reserving.ExpectedLoss()

times its exposure, whatever has been observed. The chain ladder is still fitted for the development pattern the fit reports.

The exposure is a measure column of the same triangle (premium, say): each origin’s latest observed cumulative value in the segment fitted.

Parameters

apriori: float = 1.0: Expected loss ratio: the ultimate per unit of exposure; positive.
average: (volume, simple, regression) = "volume": How link ratios are averaged, as in ChainLadder.
sigma_interpolation: (log - linear, mack) = "log-linear"
tail: (float, TailConstant, TailCurve, TailBondy or TailLogLinear): As ChainLadder; no tail by default.

Examples

>>> from prospicio.reserving import ExpectedLoss, Triangle
>>> tri = Triangle.from_long(
...     [2020, 2020, 2021], [12, 24, 12],
...     {"paid": [100.0, 150.0, 200.0], "premium": [250.0, 250.0, 400.0]},
... )
>>> fit = ExpectedLoss(apriori=0.5).fit(tri, "paid", "premium")
>>> fit.ultimate, fit.reserve

([125.0, 200.0], [-25.0, 0.0])

Attributes

Name	Description
apriori	Expected loss ratio.
average	How link ratios are averaged.
sigma_interpolation	How unestimable variance parameters are filled in.
tail	The tail: a constant factor as a number, otherwise its estimator.

apriori

Expected loss ratio.

apriori: float

average

How link ratios are averaged.

average: str

sigma_interpolation

How unestimable variance parameters are filled in.

sigma_interpolation: str

tail

The tail: a constant factor as a number, otherwise its estimator.

tail: Any

Methods

Name	Description
fit()	Fits one loss column in every segment of a triangle, each with its

fit()

Fits one loss column in every segment of a triangle, each with its

Usage

fit(triangle, column, exposure)

own exposure.

Parameters

triangle: Triangle
column: str: The losses to project.
exposure: str: The exposure column; each origin’s latest observed cumulative value is its exposure (an incremental triangle’s is cumulated).

Returns

ExpectedLossFit

Raises

ValueError: As ChainLadder.fit, if apriori is not positive, or if an origin has no observed, finite, positive exposure (the message names it and, with keys, its segment).