Runs the three blocks of the usual LGD validation on the hold-out sample
(or on newdata) against the training reference:
Arguments
- x
An
scr_lgd()object.- newdata
NULL(the hold-out), anscr_workout()object or a table with the drivers,lgd_real,eadand the default date.
Value
An object of class scr_lgd_validation: calibration (per
pool), portfolio, discrimination, stability (pools, drivers),
homogeneity, heterogeneity, summary (test, statistic, p, light;
the light is "grey" when the test has no result, and a row that sums
up several pools or drivers is the worst of their lights: red, then
amber, then green, otherwise grey), sample, n.
Details
Calibration. Per pool and for the portfolio, the one-sided t-test of realized against estimated LGD (the pool long-run average), where under-estimation is the failure:
p = 1 - Phi(t); the loss shortfall1 - sum(LGD_real E) / sum(LGD_pred E); the coverage of the realized mean by the downturn LGD; the regression of realized on predicted.Discrimination. Somers' D / generalized AUC of the prediction with its bootstrap interval, compared with the training value through
S = (gAUC_init - gAUC_curr) / sigma_curr; Spearman rho; the loss capture ratio; R-squared.Stability. PSI of the pool distribution and of the bins of every driver of both stages, with the fixed and the n-adjusted threshold.
Pools. Homogeneity within a pool (Welch test between the halves of the pool split at its median prediction; a small p-value means a pool that still discriminates) and heterogeneity between adjacent pools (Welch test; a large p-value means pools that do not differ).
Traffic lights use the p-value thresholds of config$pd_lights (shared
with the PD validation) (red at or below the
first, amber at or below the second) and the fixed PSI thresholds.
See also
Other irb-lgd:
scr_elbe(),
scr_lgd(),
scr_lgd_downturn(),
scr_lgd_floor(),
scr_lgd_pools(),
scr_workout()
Examples
cfg <- scr_config(verbose = FALSE, nthread = 1, n_boot = 20)
wo <- scr_workout(scr_demo_lgd, scr_demo_lgd_cashflows, rates = scr_demo_rates, config = cfg)
m <- scr_lgd(wo, drivers = c("product", "ltv", "prior_dpd_max"), config = cfg)
v <- scr_lgd_validate(m)
v
#> <scr_lgd_validation> sample holdout | n 265
#> calibration: realized 41.7% vs estimate 39.7% | t 1.03 p 0.152 [green] | loss shortfall -0.9% | downturn covers: TRUE
#> discrimination: gAUC 0.686 [0.647, 0.726] vs initial 0.655 (S -1.53, p 0.937) [green] | Spearman 0.530 | LCR 0.473
#> stability: pool PSI 0.0014 (stable; adjusted stable) | drivers: prior_dpd_max_cure 0.009, product_sev 0.001, prior_dpd_max_sev 0.001
#> calibration_portfolio_t green
#> calibration_pools_t red
#> loss_shortfall amber
#> downturn_coverage green
#> gauc_vs_initial green
#> psi_pools green
#> psi_drivers green
#> homogeneity_within_pools red
#> heterogeneity_between_pools red
v$calibration
#> pool n ead lgd_est lgd_dt real_mean real_ew real_lo
#> <int> <int> <num> <num> <num> <num> <num> <num>
#> 1: 1 81 8865610.8 0.2498970 0.4251785 0.2349736 0.2399839 0.1867959
#> 2: 2 74 4280608.7 0.3508759 0.5366786 0.3765230 0.4105957 0.3118399
#> 3: 3 43 531740.4 0.4747053 0.6767146 0.4580201 0.5250093 0.3565899
#> 4: 4 67 3007941.1 0.5750268 0.7699414 0.6535475 0.5271459 0.5909326
#> real_hi t p dt_covers light
#> <num> <num> <num> <lgcl> <char>
#> 1: 0.2831513 -0.6071256 0.728116210 TRUE green
#> 2: 0.4412062 0.7771488 0.218535493 TRUE green
#> 3: 0.5594504 -0.3224176 0.626431831 TRUE green
#> 4: 0.7161625 2.4578895 0.006987808 TRUE red