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Print the tables on pages 64, 65, and 68 of Clark's paper.

Usage

Table64(x)
Table65(x)
Table68(x)

Arguments

x

an object resulting from ClarkLDF or ClarkCapeCod

Details

These exhibits give some of the details behind the calculations producing the estimates of future values (a.k.a. "Reserves" in Clark's paper). Table65 works for both the "LDF" and the "CapeCod" methods. Table64 is specific to "LDF", Table68 to "CapeCod".

Value

A data.frame.

References

Clark, David R., "LDF Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach", Casualty Actuarial Society Forum, Fall, 2003 https://www.casact.org/sites/default/files/database/forum_03fforum_03ff041.pdf

Author

Daniel Murphy

Examples

Table65(ClarkLDF(GenIns, maxage=20))
#>    Origin CurrentValue FutureValue ProcessSE ProcessCV ParameterSE ParameterCV
#> 1       1      3901463    666530.8  209582.5      31.4    156593.2        23.5
#> 2       2      5339085   1157423.1  275610.3      23.8    254781.7        22.0
#> 3       3      4909315   1365086.0  298810.1      21.9    295848.6        21.7
#> 4       4      4588268   1665693.7  329605.5      19.8    353535.0        21.2
#> 5       5      3873311   1886481.4  350361.0      18.6    397791.3        21.1
#> 6       6      3691712   2514880.3  404131.2      16.1    513654.8        20.4
#> 7       7      3483130   3546784.7  479542.3      13.5    698509.6        19.7
#> 8       8      2864498   4896501.4  563084.2      11.5    961057.8        19.6
#> 9       9      1363294   4991635.1  568138.4      11.4   1218732.2        24.4
#> 10     10       344014   6223706.3  634046.3      10.2   2821745.5        45.3
#>     Total     34358090  28914722.7 1369565.1       4.7   4651504.5        16.1
#>     StdError TotalCV
#> 1   261622.3    39.3
#> 2   375332.8    32.4
#> 3   420492.4    30.8
#> 4   483349.5    29.0
#> 5   530085.6    28.1
#> 6   653577.3    26.0
#> 7   847275.9    23.9
#> 8  1113865.3    22.7
#> 9  1344652.2    26.9
#> 10 2892103.5    46.5
#>    4848938.3    16.8
Table64(ClarkLDF(GenIns, maxage=20))
#>    Origin CurrentValue CurrentAge AgeUsed GrowthFunction       Ldf TruncatedLdf
#> 1                   NA         20    19.5     0.90550697  1.104354     1.000000
#> 2       1      3901463         10     9.5     0.77338151  1.293023     1.170841
#> 3       2      5339085          9     8.5     0.74418113  1.343759     1.216783
#> 4       3      4909315          8     7.5     0.70850094  1.411431     1.278060
#> 5       4      4588268          7     6.5     0.66433229  1.505271     1.363033
#> 6       5      3873311          6     5.5     0.60892995  1.642225     1.487046
#> 7       6      3691712          5     4.5     0.53860005  1.856665     1.681223
#> 8       7      3483130          4     3.5     0.44865388  2.228890     2.018275
#> 9       8      2864498          3     2.5     0.33421249  2.992108     2.709375
#> 10      9      1363294          2     1.5     0.19425429  5.147891     4.661452
#> 11     10       344014          1     0.5     0.04743002 21.083696    19.091433
#> 12  Total     34358090         NA      NA             NA        NA           NA
#>    LossesAtMaxage FutureValue
#> 1              NA          NA
#> 2         4567994    666530.8
#> 3         6496508   1157423.1
#> 4         6274401   1365086.0
#> 5         6253962   1665693.7
#> 6         5759792   1886481.4
#> 7         6206592   2514880.3
#> 8         7029915   3546784.7
#> 9         7760999   4896501.4
#> 10        6354929   4991635.1
#> 11        6567720   6223706.3
#> 12       63272813  28914722.7

X <- GenIns
colnames(X) <- 12*as.numeric(colnames(X))
Table65(ClarkCapeCod(X, Premium=10000000+400000*0:9, maxage=Inf))
#>    Origin CurrentValue FutureValue ProcessSE ProcessCV ParameterSE ParameterCV
#> 1       1      3901463     1323698  284493.9      21.5    340851.2        25.7
#> 2       2      5339085     1556996  308547.7      19.8    380563.1        24.4
#> 3       3      4909315     1846297  335992.3      18.2    424217.1        23.0
#> 4       4      4588268     2209996  367598.9      16.6    471286.9        21.3
#> 5       5      3873311     2673721  404330.5      15.1    520246.7        19.5
#> 6       6      3691712     3272829  447342.5      13.7    567800.6        17.3
#> 7       7      3483130     4053931  497870.8      12.3    607956.3        15.0
#> 8       8      2864498     5069599  556756.5      11.0    632156.8        12.5
#> 9       9      1363294     6344850  622858.1       9.8    635453.9        10.0
#> 10     10       344014     7738201  687857.5       8.9    642428.6         8.3
#>     Total     34358090    36090118 1485499.9       4.1   5169409.8        14.3
#>     StdError TotalCV
#> 1   443977.8    33.5
#> 2   489928.5    31.5
#> 3   541157.1    29.3
#> 4   597695.8    27.0
#> 5   658892.9    24.6
#> 6   722850.5    22.1
#> 7   785802.9    19.4
#> 8   842377.6    16.6
#> 9   889805.5    14.0
#> 10  941202.7    12.2
#>    5378615.7    14.9
Table68(ClarkCapeCod(X, Premium=10000000+400000*0:9, maxage=Inf))
#>    Origin  Premium CurrentAge AgeUsed GrowthFunction FutureGrowthFactor
#> 1               NA        Inf     Inf     1.00000000          0.0000000
#> 2       1 1.00e+07        120     114     0.77828466          0.2217153
#> 3       2 1.04e+07        108     102     0.74923842          0.2507616
#> 4       3 1.08e+07         96      90     0.71365834          0.2863417
#> 5       4 1.12e+07         84      78     0.66949336          0.3305066
#> 6       5 1.16e+07         72      66     0.61393113          0.3860689
#> 7       6 1.20e+07         60      54     0.54317615          0.4568238
#> 8       7 1.24e+07         48      42     0.45240260          0.5475974
#> 9       8 1.28e+07         36      30     0.33660779          0.6633922
#> 10      9 1.32e+07         24      18     0.19489202          0.8051080
#> 11     10 1.36e+07         12       6     0.04696731          0.9530327
#> 12  Total 1.18e+08         NA      NA             NA                 NA
#>    PremiumxELR FutureValue
#> 1           NA          NA
#> 2      5970260     1323698
#> 3      6209071     1556996
#> 4      6447881     1846297
#> 5      6686692     2209996
#> 6      6925502     2673721
#> 7      7164313     3272829
#> 8      7403123     4053931
#> 9      7641933     5069599
#> 10     7880744     6344850
#> 11     8119554     7738201
#> 12    70449074    36090118