B.02.Miccolis

  1. Average Basic Limit Severity (ABLS) vs ILFs
    • ABLS = E[g(x;L)]
    • I'(k) = [1 - F(k)] / ABLS
    • F(x) → 1 as x → ∞
    • I'(k) → 0 as k → ∞
    • I(k) must approach a constant as k → ∞
    • past some large limit, there is no additional charge
    • I'(k) must decrease monotonically
  2. Using ILFs to price XS layers
    PP = E(N)(E[g(x;s)] - E[g(x;r)]) = E(N)ABLS(I(s) - I(r))
  3. ILF separate trending
    • could trend separately avg severity of ABLS and of ILF
    • however results suggest lower inflationary factor for large losses → counterintuitive
  4. Including process risk in P
    • Miccolis prefers variance
    • Premium = E(Y) + λVar(Y)
    • λ chosen judgmentally
    • Risk adj P = E[g(x;k)] + λE[g(x;k)2]
    • E[h(x;r,j)2] = E[g(x;s)2] - E[g(x;r)2] - 2jE[h(x;r,j)]
  5. Difficulties in creating a severity distribution from empirical data
    • have to consider development on any set of clms
    • data often comes from policies w different limits → bias
    • credibility of the dist at high end might be low
  6. Examples of parameter risk
    • catastrophes such as hurricanes, tornadoes, eq, ...
    • change in mix of business
    • small insurers face sampling errors
    • incorrect ratemaking data
    • claims practices
    • uw practices
    • social attitudes
    • judicial or legislative climate
  7. 2 dimensional ILF consistency test
    • Test 1: marginal premium per $1,000 should decr as limit incr
    • Test 2: for any 2 occurence limits, diff in ILF must not decr as aggregate limit incr
  8. Anti-selection
    • adverse: high limit = worse experience because of (1) insd who expect high loss buy high limits and (2) lawsuits influenced by limit
    • favorable: high limit = better experience because of (1) financially secure buy high limit because they have more assets and (2) insr is willing to insure good risks at higher limtis
    • in some case affects ILF
  9. ILFs vs coinsurance
    • if insure x%, P > x% PP
    • E[g(x;x%k)] > x%E[g(x;k)]
    • P = full value exposure units x coins % x coins factor x rate
  10. Rosenberg's consistency test
    • A1<2 = aggregate limit, O1<2 = occurence limit
    • A1 to A2 adds at least as much exposure at O2 as for O1
    • O1 to O2 adds a least as much exposure at A2 as for A1
    • for any 2 aggregate limits, diff in factors must not decrease as occurence limit grows
Author
Exam8
ID
162903
Card Set
B.02.Miccolis
Description
On the theory of increased limits and excess of loss pricing
Updated