Standardised distance from expected claims to the aggregate attachment point

Expresses the gap between the aggregate attachment point and expected net claims in standard deviations of total claims. Under a normal approximation to total claims, the probability that claims exceed the attachment point is 1 minus Phi(z), where Phi is the standard normal cumulative distribution function; Excel returns it as 1-NORM.S.DIST(z,TRUE). The calculator on this page returns z, since it has no normal distribution function.

Signature

z = (A - E_S) / SD_S
Inputs
InputsDefinitionUnit
AAggregate attachment point for the contract yeardollars
E_SExpected annual net claims of the groupdollars
SD_SStandard deviation of the group's annual net claimsdollars
Output
zNumber of standard deviations of total claims by which the attachment point lies above expected claimsstandard deviations

Function

Aggregate stop-loss payment and expected payment function

Maps a self-funded employer's eligible claims for the contract year, net of specific stop-loss recoveries, to the payment due from the aggregate stop-loss carrier and the claims the employer retains, given the aggregate attachment point and any maximum aggregate payment. Applied to the distribution of the year's claims instead of one realised year, the same rule gives the carrier's expected payment, the net stop-loss premium before any loading. The records follow the notation of the Aggregate Stop-Loss article, where S is net claims for the year, A is the aggregate attachment point and L is the carrier's payment.

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Implementations

  • Excel

    Standardised distance and tail probability for the aggregate attachment point

    The first formula returns z from named cells AttachPoint, ExpClaims and SDClaims; the second returns the normal approximation to the probability of reaching the attachment point from the cell holding z, named ZScore.

    =(AttachPoint-ExpClaims)/SDClaims; =1-NORM.S.DIST(ZScore,TRUE)

Assumptions

  • Normal approximation to a group's total net claims

    Reading z as a tail probability assumes total net claims are approximately normal. Claims are skewed and can share common shocks, so the approximation understates the upper tail, especially for small groups, and the result shows the direction of an effect rather than a price.

Worked examples

  • Distance to the attachment point for a 200-member group

    Employer 1's attachment point lies $375,000 above expected claims, about 1.7678 standard deviations. The normal approximation puts the chance of reaching it at about 0.0385, roughly one year in 26.

    A = 1875000; E_S = 1500000; SD_S = 212132.03; z = 1.7678
  • Distance to the attachment point for a 20-member group

    Employer 2's attachment point lies $37,500 above expected claims, about 0.5590 standard deviations. The normal approximation puts the chance of reaching it at about 0.2881, a little more than one year in four and more than seven times Employer 1's chance.

    A = 187500; E_S = 150000; SD_S = 67082.04; z = 0.5590

Common errors

  • Reversing the sign of the standardised distance

    Computing z as expected claims minus the attachment point gives minus 1.7678 for Employer 1, and 1-NORM.S.DIST then returns about 0.9615 instead of 0.0385, so the carrier appears to pay in almost every year.

  • Reading the normal tail probability as reliable for a small group

    For 20 members the claims distribution is strongly skewed, and a few very costly members or a shared shock can push claims far above the attachment point more often than the normal curve implies. The 0.2881 figure for Employer 2 is a lower guide, not an estimate to price on.

Sources

  • Normal approximation to the distribution of total claims

    Kaas R, Goovaerts M, Dhaene J, Denuit M. Modern Actuarial Risk Theory: Using R. 2nd ed. Berlin: Springer; 2008. Chapter 2, The individual risk model, which studies the distribution of the total claim amount of a portfolio, the probability that the amounts paid exceed a fixed threshold, and approximations to that distribution, especially in the far tail.

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Canonical Identity