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Creating series of simulation results

sicr_series.Rd

This function executes sicr n times and returns a result series of each realisation for the desired columns.

Usage

sicr_series(
  n,
  columns = c("Claim_payments"),
  drop_ibnr_claims = TRUE,
  large_claims_list,
  first_orig_year,
  last_orig_year,
  pools,
  indices,
  history,
  exposure = NULL,
  years_for_ibnr_pools = NULL,
  active_annuities = NULL,
  age_shift = NULL,
  mortality = NULL,
  reinsurance = NULL,
  seed = 12345,
  progress = TRUE
)

Arguments

n

Integer. Desired number of realisations.

columns

Character vector containing the names of the desired information. Valid names are the names of the matrices in the output of sicr(), see Value. Default: c("Claim_payments")

drop_ibnr_claims

TRUE if only known claims shall appear in the output. Default: TRUE

large_claims_list

Dataframe with one row per known large claim generated by generate_claims_list().

first_orig_year

First origin year with complete history.

last_orig_year

Last origin year.

pools

List as output of generate_pools().

indices

Dataframe for indexation, see details of prepare_data().

history

numeric matrix with one row per claim with historic payments, generated by generate_history_per_claim().
If generated in other ways, note that colnames must be the origin years.
This matrix is needed to calculate ibnr payments and reinsurance if desired.

exposure

Dataframe that must contain one row for each origin year between first_orig_year and last_orig_year and the columns Origin_year and Exposure.
Default: NULL. In this case, a constant exposure is assumed.

years_for_ibnr_pools

Vector containing the calendar years that shall be used to build the pools.
Default: NULL. In this case, all years are used for building ibnr pools.

active_annuities

Dataframe of active annuities, see active_annuities_xmpl. Default: NULL.
Only necessary if annuities shall be considered.

age_shift

Dataframe, see description of age_shift_xmpl. Default: NULL
Only necessary if annuities shall be considered.

mortality

Dataframe, see description of mortality_xmpl. Default: NULL
Only necessary if annuities shall be considered.

reinsurance

Dataframe with reinsurance information, see details of xl_cashflow() or reinsurance_xmpl. Default: NULL
Only necessary if reinsurance shall be considered.

seed

Default: 12345. Setting a seed makes the simulation reproducible.

progress

Default: TRUE. Set to FALSE if no progress bar shall be shown during simulation.

Value

Numeric three-dimensional array with

  • one row per known claim followed by one row per expected ibnr claim if desired

  • 250 columns for future calendar years

  • one matrix for each of the n desired realisations The entries contain the sum of the desired information stated in columns.

Examples

# this example uses data provided with this package
extended_claims_data <- prepare_data(claims_data = claims_data_xmpl,
                                     indices = indices_xmpl,
                                     threshold = 400000,
                                     first_orig_year = 1989,
                                     last_orig_year = 2023,
                                     expected_year_of_growing_large = 3,
                                     reserve_classes = c(1, 200001, 400001, 700001, 1400001),
                                     pool_of_annuities = pool_of_annuities_xmpl)

pools <- generate_pools(extended_claims_data = extended_claims_data,
                        reserve_classes = c(1, 200001, 400001, 700001, 1400001),
                        years_for_pools = 2014:2023,
                        start_of_tail = 17,
                        end_of_tail = 50,
                        lower_outlier_limit = -Inf,
                        upper_outlier_limit = Inf,
                        pool_of_annuities = pool_of_annuities_xmpl)

large_claims_list <- generate_claims_list(extended_claims_data = extended_claims_data,
                                          first_orig_year = 1989,
                                          last_orig_year = 2023)

history <- generate_history_per_claim(data = extended_claims_data,
                                      column = "Cl_payment_cal",
                                      first_orig_year = 1989,
                                      last_orig_year = 2023)

series <- sicr_series(n = 3,
                      large_claims_list = large_claims_list,
                      first_orig_year = 1989,
                      last_orig_year = 2023,
                      pools = pools,
                      indices = indices_xmpl,
                      history = history)
#> 
 1 of 3 (33.33333%) --- remaining time approx. Inf secs          
 2 of 3 (66.66667%) --- remaining time approx. 0,28 secs          
 3 of 3 (100%) --- remaining time approx. 0,17 secs