Fisher - Retro Flashcards
aggregate loss distribution:
area under curve, area above max ratable loss, area below min ratable loss
- total area under curve is equal to expected losses per policy
- area above max ratable limit asymptote represents expected aggregate losses in excess of max ratable limit
- area below min ratable limit asymptote represents expected shortfall of aggregate losses below min ratable limit
calculations so far have assumed that risk we are pricing is exactly the same size as risks underlying the distribution
-if new risk is slightly bigger or smaller,
- want to make an adjustment for this otherwise expected losses will be off
- to deal with this, re-scale vertical axis by dividing actual losses by expected loss
- new values on y axis are entry ratios, r, which are just ratios of actual loss to expected loss
- area under curve is 1
table M charge & table M savings
expected % of loss in excess of max ratio
expected % of loss short fall min ratio
in practice, separate tableM will be built for different risk size groups since
variance of aggregate loss distribution will vary by risk size
with tables specified in terms of entry ratios and charges, tables are less vulnerable to
inflation
-as risk increases in size due to inflation, it can simply be mapped to different existing table M charge column this is more appropriate for its new risk size
Table M Charges and Risk Size
- for smaller risk sizes, majority or risks have no claims at all but small number or risks can have 1 or 2 large claims
- for very large risks, all risks will have claims and experience across all risks becomes more similar as there is less variance in loss experience between risks
- as risk size goes to infinity, variance in entry ratios goes to 0 and curve will flatten to look like all risks have exact same amount of losses
to summarize about errors in insurance charges
- % error in insurance charges is greatest for large policies with high entry ratios
- $ error in insurance charge is greatest for large policies with low entry ratios
asymptote approached by very large risks for table M charge and savings
φ(r) = max(1-r,0)
ψ(r) = max(r-1,0)
as risk size goes to 0 for table M charge and savings
φ(r) -> 1
ψ(r) ->0
- some policies of even same size are riskier than other policies and should account for this
- to do this,
adjust expected losses for risk to match those of a risk with different size but similar variance in aggregate loss distribution
example of this, historically NCCI has adjusted for riskiness differences between states and hazard groups
basic premium formula derived contains net insurance charge I
when max and min premiums are explicitly selected, net insurance charge depends on
max and min premium selected
- max and min premiums also depend on basic premium
- so trial and error procedure called table M search is needed to determine correct Table M rows for rating a policy
when per occurrence limit is applicable,
table M distributions will no longer be appropriate as they don’t recognize overlap between occurrence limit and aggregate limit
occurrence limit reduces
the variance of aggregate loss distribution; this is because variance of underlying severity distribution is reduced by occurrence limit
-in general, the smaller the limit, the less variance in severity distribution, so limited aggregate distribution will have less variance as well
have 2 options for how to deal with estimating excess losses with per occurrence limit
- can estimate per occurrence excess loss separately from limited aggregate excess loss; obtain expected limited aggregate excess loss using a limited table M which is identical to table M built using limited loss data instead of unlimited loss data
- can estimate per occurrence excess losses and limited aggregate excess losses simultaneously; can obtain these amounts using a table L where table L charge will include charge for both per occurrence expected excess losses as well as limited aggregate excess losses
limited table M
- is the same as creating a regular table M but you use limited losses for each policy instead of unlimited losses and a separate table is built for each occurrence limit
- diagram looks the same except now vertical axis represents limited losses or limited entry ratios
- all same methods for calculating the areas can be used and properties of tables are basically identical
since presence of occurrence limit would normally require entirely new tables (varying by occurrence limit) compared to table M, approximation can be used to simulate limited table M by
adjusting column used from a regular table M
can use insurance charge reflecting loss limitation procedure
insurance charge reflecting loss limitation procedure
- use procedure to change table M column used for risk with occurrence limit to be column that normally be used by larger risk in absence of occurrence limit
- to perform, add an extra term called loss group adjustment factor into calculation of adjusted expected loss for risk
- if you use procedure, you are still simulating a limited table M, you will use limited table M balance equations instead of regular table M balance equations to perform your table M search