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Actuarial Modelling of Claim Counts Risk Classification, Credibility ...

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270 <strong>Actuarial</strong> <strong>Modelling</strong> <strong>of</strong> <strong>Claim</strong> <strong>Counts</strong><br />

Table 6.3 Evolution <strong>of</strong> relativities and pure premiums (taking the average cost <strong>of</strong> a claim with<br />

material damage only (mat) as the monetary unit, and assuming that claims with bodily injuries (bod)<br />

are on average ten times more expensive) if a single claim with material damage only has been reported<br />

during the first year.<br />

Time Good driver Bad driver<br />

Relativity<br />

mat<br />

Relativity<br />

bod<br />

Pure<br />

premium<br />

Relativity<br />

mat<br />

Relativity<br />

bod<br />

Pure<br />

premium<br />

1 171.6 % 152.0 % 29.0 % 150.7 % 134.9 % 77.0 %<br />

2 160.3 % 142.8 % 27.2 % 127.3 % 115.7 % 65.6 %<br />

3 150.4 % 134.7 % 25.6 % 110.1 % 101.6 % 57.2 %<br />

4 141.7 % 127.6 % 24.1 % 97.0 % 90.7 % 50.8 %<br />

5 133.9 % 121.2 % 22.9 % 86.7 % 82.2 % 45.7 %<br />

6 126.9 % 115.5 % 21.7 % 78.3 % 75.2 % 41.6 %<br />

7 120.6 % 110.3 % 20.7 % 71.4 % 69.4 % 38.1 %<br />

8 114.9 % 105.7 % 19.8 % 65.6 % 64.6 % 35.3 %<br />

9 109.7 % 101.4 % 18.9 % 60.7 % 60.4 % 32.8 %<br />

10 105.0 % 97.5 % 18.2 % 56.5 % 56.8 % 30.7 %<br />

Table 6.4 Evolution <strong>of</strong> relativities and pure premiums (taking the average cost <strong>of</strong> a claim with<br />

material damage only (mat) as the monetary unit, and assuming that claims with bodily injuries (bod)<br />

are on average ten times more expensive) if a single claim with bodily injuries has been reported<br />

during the first year.<br />

Time Good driver Bad driver<br />

Relativity<br />

mat<br />

Relativity<br />

bod<br />

Pure<br />

premium<br />

Relativity<br />

mat<br />

Relativity<br />

bod<br />

Pure<br />

premium<br />

1 150.7 % 201.9 % 32.1 % 131.7 % 185.5 % 87.3 %<br />

2 140.5 % 193.1 % 30.4 % 110.4 % 166.8 % 76.6 %<br />

3 131.5 % 185.4 % 28.9 % 94.8 % 152.9 % 68.6 %<br />

4 123.5 % 178.5 % 27.6 % 82.9 % 142.0 % 62.4 %<br />

5 116.5 % 172.3 % 26.4 % 73.6 % 133.2 % 57.5 %<br />

6 101.1 % 166.7 % 25.3 % 66.0 % 125.8 % 53.5 %<br />

7 104.4 % 161.7 % 24.3 % 59.8 % 119.6 % 50.1 %<br />

8 99.2 % 157.0 % 23.5 % 54.6 % 114.3 % 47.3 %<br />

9 94.5 % 152.8 % 22.7 % 50.2 % 109.6 % 44.8 %<br />

10 90.2 % 148.9 % 21.9 % 46.4 % 105.5 % 42.6 %<br />

6.3 Multi-Event Bonus-Malus Scales<br />

6.3.1 Types <strong>of</strong> <strong>Claim</strong>s<br />

Here we adopt the same assumptions as in Chapter 4. Let us pick a policyholder at random<br />

from the portfolio and let us denote as N the number <strong>of</strong> claims reported during the year.<br />

Furthermore, let be the (unknown) a priori expected claim frequency, with Pr = k = w k .

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