Fits the standard two-stage structure on the RDS of scr_workout():
$$\mathrm{LGD} = P(\mathrm{cure}\mid x)\,\mathrm{LGD}^{\mathrm{cure}} + \big(1 - P(\mathrm{cure}\mid x)\big)\,\mathrm{E}[\mathrm{LGD}\mid \mathrm{no\ cure}, x]$$
The cure stage is a binary model on is_cure with the scorecard
machinery: optimal binning of the drivers on the training cohorts, WOE,
hold-out revalidation with frozen bins and a logistic regression on the
WOE columns with the sign check (every coefficient positive). The
severity stage bins the same drivers against the realized LGD of
the non-cures with scr_bin_continuous() (bin means, monotone, at least
lgd_min_defaults_bin defaults per bin, hold-out revalidated) and fits a
fractional logit (glm with a quasi-binomial family on the bin means)
or, with lgd_severity = "beta", a beta regression through the
betareg package. LGD^cure is the mean realized LGD of the cures on
train (costs and the discount effect, never zero by decree).
Usage
scr_lgd(
x,
drivers,
config = scr_config(),
holdout = 0.3,
date_col = "default_date"
)Arguments
- x
An
scr_workout()object.- drivers
Column names of the RDS to use as drivers.
- config
A
scr_config(); keyslgd_*, the binning and hold-out keys of stage 2,max_abs_coef,n_boot,ci_level,seed,nthread.- holdout
Share of the cohorts held out (by default date).
- date_col
Column of the RDS with the default date.
Value
An object of class scr_lgd: split, drivers, cure (fit,
features, coef, sign_check, bins, holdout), severity (fit, features,
coef, engine, sign_check, bins), lgd_cure, has_cures, scored (one
row per default: sample, p_cure, severity, lgd_pred, pool,
lgd_real), bins_idx, samples (predicted vs realized by decile of
the prediction), metrics, pools, downturn, floors, workout
(the profile and summary of the RDS), model_card, ledger, config.
Details
The split is by cohort of default: the last holdout share of the
default dates is the hold-out. Metrics on both samples: RMSE, MAE,
R-squared, Spearman rho, Somers' D of the prediction with respect to
the realized LGD (generalized AUC (D + 1) / 2) with a bootstrap
confidence interval, and the loss capture ratio. Pools come from
scr_lgd_pools(). The object carries a provisional downturn (type 3
add-on, or none, by configuration) and no floor until
scr_lgd_downturn() and scr_lgd_floor() run.
See also
Other irb-lgd:
scr_elbe(),
scr_lgd_downturn(),
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", "months_on_book", "region"),
config = cfg)
m
#> <scr_lgd> 885 defaults | train 620 / hold-out 265 (cohort split after 2024-01-01) | cure rate 37.5%
#> cure stage: prior_dpd_max, months_on_book, region | severity stage (fractional_logit): product, prior_dpd_max, months_on_book | LGD of a cure 4.5%
#> train n 620 RMSE 0.2731 R2 0.241 Spearman 0.473 gAUC 0.672 [0.657, 0.686] LCR 0.403
#> holdout n 265 RMSE 0.2655 R2 0.275 Spearman 0.536 gAUC 0.693 [0.644, 0.725] LCR 0.460
#> pools 3 | downturn type1 (provisional) | floor not applied
#> pool n pred LRA LRA ew MoC C LGD DT floor final
#> 1 274 0.276 27.5% 23.1% 0.023 44.8% 0.000 44.8%
#> 2 170 0.415 40.0% 38.0% 0.038 58.8% 0.000 58.8%
#> 3 176 0.554 59.3% 42.8% 0.043 78.6% 0.000 78.6%
m$pools[, c("pool", "n", "lra", "lra_ew", "moc_c", "lgd_dt")]
#> pool n lra lra_ew moc_c lgd_dt
#> <int> <int> <num> <num> <num> <num>
#> 1: 1 274 0.2749072 0.2306159 0.02281440 0.4477216
#> 2: 2 170 0.4002597 0.3802176 0.03786551 0.5881253
#> 3: 3 176 0.5926373 0.4275209 0.04292232 0.7855596
m$metrics
#> Index: <sample>
#> sample n rmse mae r2 spearman somers_d gauc
#> <char> <int> <num> <num> <num> <num> <num> <num>
#> 1: train 620 0.2730699 0.2445794 0.2411368 0.4725775 0.3442493 0.6721247
#> 2: holdout 265 0.2655118 0.2350532 0.2751904 0.5359311 0.3850772 0.6925386
#> lcr somers_lo somers_hi gauc_lo gauc_hi lcr_lo lcr_hi n_boot
#> <num> <num> <num> <num> <num> <num> <num> <int>
#> 1: 0.4030191 0.3132275 0.3713491 0.6566138 0.6856745 0.3177067 0.5074543 20
#> 2: 0.4603287 0.2875191 0.4506452 0.6437596 0.7253226 0.3627325 0.5483924 20
#> level
#> <num>
#> 1: 0.95
#> 2: 0.95