Fall 2018 Q13 part a.ii)

Hi Graham - The question asks for rate per $1000 which is generally pretty straight forward. This particular question introduces a curveball with a loss distribution that varies per interval. I understand it except for part a) ii) where we have to assume the AOI = $800K. I don't understand the calculation of the $766.67 in the average severity calculation for the 700K < X < 850K interval from the examiner's report. I get why the average severity is calculated different but I don't understand how it's calculated. I thought it would be $750K instead by doing (700+800)/2 but that's wrong. Can you help me think through this? Perhaps it will make the linear interpolation component below make more sense. Thanks.

Avg Severity = .5(200K) + .25(475K) + .1(625K) + .1(766.667K) + (.025 + .025)(800K) = $397.917K

Where 766.667K from above is calculated: 750K*(2/3)+800K(1/3)

Comments

  • Hello @brb2241

    I'm sorry I didn't see your question until today. I will provide an answer sometime Friday.

    @Graham

  • The trick here is that to calculate the average severity in the larger interval (700, 850), you have to break that interval into 2 smaller intervals as follows:

    • (700, 800)
    • (800, 850)

    The reason the breakpoint is 800 is that AOI = 800 for part (ii). Then:

    • average severity for the interval (700, 800) is 750
    • average severity for the interval (800, 850) is 800 (due to the AOI cap of 800)

    Then you have to observe that for all losses that fall in the larger interval (700, 850), we would have 2/3 of them in the interval (700, 800), and 1/3 in the interval (800, 850) - because the severity distribution is uniform. From there you can calculate the average severity for (700, 850) as a weighted average:

    • (2/3 x 750) + (1/3 x 800) = 766.67

    If the AOI in part (ii) had fallen on the end of one of the intervals for the size of loss distribution (Ex: 850) then you wouldn't have had to do that. But since it fell in the middle, you had to do the extra step as explained above.

  • Got it, thanks!

  • You're welcome! 😀

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