Build the realized-CCF reference data set from facility snapshots

One row per default event (or per event and reference date under the variable-horizon comparison), with the facility as it stood at the reference date and the realized exposure at default (EAD) at the default date, from which the realized credit conversion factor (CCF) follows. The reference date follows config$ccf_horizon: "fixed" takes the snapshot ccf_horizon_months before the default month (the nearest earlier snapshot when that month is missing; the first snapshot for a facility younger than the horizon, flagged FAST_DEFAULT); "cohort" takes the start of the calendar cohort window in which the default falls; "variable" takes every snapshot in the horizon before the default, for comparison only.

Usage

scr_ead_data(
  snapshots,
  facility_id,
  obligor_id = NULL,
  date_col,
  limit,
  drawn,
  default_date = NULL,
  defaulted = NULL,
  drivers = NULL,
  config = scr_config(),
  keep_rows = FALSE
)

Arguments

snapshots: A data.frame or data.table with one row per facility and month.
facility_id, date_col, limit, drawn: Column names of the facility identifier, the snapshot month, the limit and the drawn amount.
obligor_id: Column name of the obligor, optional. With config$default_level = "obligor" a default of any facility of the obligor is a default of all its facilities observed at that date.
default_date: Either the name of a column of snapshots holding the default date of the facility (NA when it never defaults) or a data.frame with the facility identifier column (same name as facility_id) and a default_date column, one row per event.
defaulted: Column name of a 0/1 default flag per snapshot, alternative to default_date: every run of ones opens an event at its first month.
drivers: Column names measured at the reference date and carried into the data set (candidate drivers of the pools). utilisation_ref, limit_ref, drawn_ref and horizon_months are always available.
config: A scr_config(); keys ccf_*, post_default_drawings_in, default_level.
keep_rows: If TRUE, keeps every candidate event with its exclusion rule in rows.

Value

An object of class scr_ead_data: rds (one row per event: event_id, facility_id, obligor_id, ref_date, default_date, cohort, horizon_months, fast_default, limit_ref, drawn_ref, undrawn_ref, utilisation_ref, limit_default, limit_change, ead_realised, measure, ccf_raw, ccf, rule, drivers), funnel (rule, action, n, share), summary (simple and exposure-weighted averages by cohort and measure, with a total row), lra (long-run averages and shares), meta, ledger, config and rows (with keep_rows = TRUE).

Details

The realized measure per row follows config$ccf_measure: under "auto" the undrawn-limit factor (CCF) when the utilization at the reference date is below ccf_u_star and the limit factor (LF) at or above it; rows with nothing undrawn or over the limit at the reference date are always routed to the limit factor (ZERO_UNDRAWN, OVER_LIMIT_AT_REF), never dropped. The raw realized value is kept in ccf_raw; ccf carries the value after the optional floor (ccf_floor_realised) and cap (ccf_cap_realised), both logged in the funnel (NEGATIVE_CCF_FLOORED, CCF_ABOVE_ONE). The realized EAD is the drawn amount at the default date, uncapped; with post_default_drawings_in = "ccf" it is the maximum drawn amount over the default event when defaulted is given.

References

Basel Committee on Banking Supervision (2023). The Basel Framework, CRE32 and CRE36. Moral, G. (2006). EAD estimates for facilities with explicit limits. In Engelmann, B. and Rauhmeier, R. (eds), The Basel II Risk Parameters. Springer.

Examples

cfg <- scr_config(verbose = FALSE)
ed <- scr_ead_data(scr_demo_ead, facility_id = "facility_id", obligor_id = "obligor_id",
                   date_col = "ref_date", limit = "limit", drawn = "drawn",
                   defaulted = "defaulted",
                   drivers = c("product", "months_on_book", "dpd"), config = cfg)
ed
#> <scr_ead_data> 197 rows from 197 default events | 1,200 facilities x 34,811 snapshots
#>   horizon: fixed (12 months) | measure: auto (u* = 0.95) | reference dates 2023-01-01 to 2024-06-01 (1.4 years)
#>   ULF: LRA simple 0.3248 | exposure-weighted 0.2889 | n = 189
#>   LF rows 4.1% | above one 7.4% | negative (raw) 23.8% | fast defaults 19.8%
#>   funnel:
#>     FAST_DEFAULT             kept              25 (12.7%)
#>     OVER_LIMIT_AT_REF        routed_to_lf       7 (3.6%)
#>     NEGATIVE_CCF_FLOORED     floored           45 (22.8%)
#>     CCF_ABOVE_ONE            kept              14 (7.1%)
#>     OK                       kept             106 (53.8%)
#>   note: fewer than five years of reference dates; the average is not a long-run one yet
ed$funnel
#>                    rule       action     n      share
#>                  <char>       <char> <int>      <num>
#> 1:         FAST_DEFAULT         kept    25 0.12690355
#> 2:    OVER_LIMIT_AT_REF routed_to_lf     7 0.03553299
#> 3: NEGATIVE_CCF_FLOORED      floored    45 0.22842640
#> 4:        CCF_ABOVE_ONE         kept    14 0.07106599
#> 5:                   OK         kept   106 0.53807107
head(ed$rds[, c("facility_id", "ref_date", "default_date", "utilisation_ref", "measure", "ccf")])
#>    facility_id   ref_date default_date utilisation_ref measure       ccf
#>         <char>     <Date>       <Date>           <num>  <char>     <num>
#> 1:       F0031 2023-01-01   2023-08-01          0.5200     ulf 0.0000000
#> 2:       F0062 2023-01-01   2023-11-01          0.4600     ulf 0.0000000
#> 3:       F0093 2023-01-01   2023-07-01          0.6485     ulf 0.1493599
#> 4:       F0103 2023-01-01   2023-12-01          0.3400     ulf 0.0000000
#> 5:       F0108 2023-01-01   2024-01-01          0.1300     ulf 0.2643678
#> 6:       F0109 2023-01-01   2023-08-01          0.3650     ulf 0.4645669