Cuts the training predictions into n_pools quantile bands, merges the
bands with fewer than min_defaults defaults into the neighbor with
the closer long-run average, then merges adjacent bands whose long-run
averages break the increasing order (pool-adjacent violators), so that
the pools are ordered both in predicted and in realized LGD. Per pool:
the default-weighted long-run average (the regulatory estimate), the
exposure-weighted one, the standard error, the category-C margin of
conservatism (one-sided 95% t interval on the mean) and their sum.
Arguments
- x
An
scr_lgd()object.- n_pools
Target number of pools;
NULLuseslgd_n_pools.- min_defaults
Minimum defaults per pool;
NULLuseslgd_min_defaults_bin.
Value
A data.table with one row per pool: pool, pred_lo,
pred_hi, pred_mean, n, share, ead, lra, lra_ew, sd,
se, moc_c, lra_moc, merged_from.
See also
Other irb-lgd:
scr_elbe(),
scr_lgd(),
scr_lgd_downturn(),
scr_lgd_floor(),
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)
scr_lgd_pools(m, n_pools = 4)
#> pool pred_lo pred_hi pred_mean n share ead lra
#> <int> <num> <num> <num> <int> <num> <num> <num>
#> 1: 1 -Inf 0.3216997 0.2518886 188 0.3032258 24831790 0.2498970
#> 2: 2 0.3216997 0.4098194 0.3850771 165 0.2661290 8375662 0.3508759
#> 3: 3 0.4098194 0.4535521 0.4364856 146 0.2354839 6628595 0.4529029
#> 4: 4 0.4535521 Inf 0.5683979 121 0.1951613 2652442 0.6336688
#> lra_ew sd se moc_c lra_moc merged_from
#> <num> <num> <num> <num> <num> <char>
#> 1: 0.2201267 0.2096992 0.01529388 0.02528145 0.2751785 1
#> 2: 0.3281958 0.2780163 0.02164354 0.03580269 0.3866786 2
#> 3: 0.3971602 0.3053699 0.02527259 0.04183702 0.4947399 3
#> 4: 0.4107764 0.3518117 0.03198288 0.05301645 0.6866853 4