Downturn LGD per pool

Quantifies the downturn per pool from user-supplied downturn periods. method = "type1" (observed impact): the default-weighted realized LGD of the training defaults whose default date falls inside the periods; a pool with fewer than ten such defaults falls back to type 3. method = "type3": the long-run average plus add_on. method = "none": the long-run average. The reference value (a challenger, not a bound) is the mean of the two worst calendar years of the pool. Both use the training rows only, so the hold-out stays independent evidence. The downturn LGD used for capital is $$\mathrm{LGD}^{DT} = \min\!\big(1,\ \max(\mathrm{LRA} + \mathrm{MoC},\ \mathrm{DT} + \mathrm{MoC})\big)$$ and the impact LGD^DT - min(1, LRA + MoC) is reported per pool.

Usage

scr_lgd_downturn(
  x,
  periods = NULL,
  method = NULL,
  add_on = NULL,
  reason = NULL
)

Arguments

x

An scr_lgd() object.

periods

A table with start and end dates of the downturn periods. Required for "type1".

method

"type1", "type3" or "none"; NULL uses lgd_downturn.

add_on

Type-3 add-on; NULL uses lgd_downturn_add_on.

reason

Free text recorded in the ledger, mandatory: the choice of periods and method is an analyst decision.

Value

The scr_lgd object with downturn (table per pool: lra, moc_c, dt_observed, n_downturn, dt_type3, reference_value, method_used, dt, lgd_dt, impact, below_reference; periods, method, add_on, status, reason) and the pool columns lgd_dt and lgd_final updated.

See also

Other irb-lgd: scr_elbe(), scr_lgd(), scr_lgd_floor(), scr_lgd_pools(), scr_lgd_validate(), scr_workout()

Examples

cfg <- scr_config(verbose = FALSE, nthread = 1, n_boot = 20)
wo <- scr_workout(scr_demo_lgd, scr_demo_lgd_cashflows, rates = scr_demo_rates, config = cfg)
m <- scr_lgd(wo, drivers = c("product", "ltv", "prior_dpd_max"), config = cfg)
m <- scr_lgd_downturn(m, periods = data.frame(start = as.Date("2022-01-01"),
                                               end = as.Date("2023-12-31")),
                      reason = "reference rate above 13% in 2022-2023")
m$downturn$table
#>     pool     n       lra      moc_c reference_value  dt_type3 dt_observed
#>    <int> <int>     <num>      <num>           <num>     <num>       <num>
#> 1:     1   188 0.2498970 0.02528145       0.2769583 0.3998970   0.2480498
#> 2:     2   165 0.3508759 0.03580269       0.4306490 0.5008759   0.4306490
#> 3:     3   107 0.4747053 0.05200935       0.5114381 0.6247053   0.4912879
#> 4:     4   160 0.5750268 0.04491468       0.6727390 0.7250268   0.6253841
#>    n_downturn method_used        dt    lgd_dt     impact below_reference
#>         <int>      <char>     <num>     <num>      <num>          <lgcl>
#> 1:         80       type1 0.2480498 0.2751785 0.00000000            TRUE
#> 2:         70       type1 0.4306490 0.4664517 0.07977312           FALSE
#> 3:         46       type1 0.4912879 0.5432972 0.01658261           FALSE
#> 4:         73       type1 0.6253841 0.6702988 0.05035731            TRUE