Loss given default (LGD)

Workout LGD and market LGD

Compare observed recovery cash flows with market-price proxies and understand their limits.

A visual overview of Compare observed recovery cash flows with market-price proxies and understand their limits. See the guide for assumptions and implementation details.

Purpose

Describe two broad ways to estimate loss severity and the data each one needs.

Key terms

Hover over or focus a term for a quick definition, or open its glossary entry.

Key definitions

Workout LGD is estimated from post-default recoveries and costs over time.
Market LGD uses market prices or traded distressed-obligation values as a proxy for economic loss.

Formula

Workout estimate: 1 − PV(net recoveries)/EAD; market proxy: 1 − observed distressed value/reference exposure, after method-specific adjustments.

Worked example

A $100 claim sold for $42 implies a raw market discount of 58%, before transaction and representativeness adjustments.

Detailed explanation

Workout data can directly reflect realized cash recovery and cost timing, but they take time to mature and can be incomplete. Market-based observations may be available faster and can reflect current conditions, but may include liquidity discounts or instruments that differ from the portfolio being modeled.

Treat the methods as data choices with distinct assumptions rather than interchangeable labels. Segment by product, seniority, collateral, jurisdiction, and recovery route where possible. Compare both against subsequent outcomes and document adjustments. If either approach is used for IFRS 9 measurement or regulatory capital, follow the applicable framework rather than importing a textbook shortcut.

Try a reproducible Python example

These examples use synthetic inputs and run locally in a browser worker. Edit the code, run it, and compare its output with the published example.

Runnable Python · local in your browser

Link re-default episodes and calculate discounted LGD

Apply a declared synthetic 12-month cure probation and compute two episode LGDs from bounded cashflow lists.

Editable Python example

from math import pow

# Synthetic example policy only: a re-default before 12 months links to the open episode.
cure_probation_months = 12
return_to_current_month = 6
redefault_month = 15
probation_complete_month = return_to_current_month + cure_probation_months
episode = 1 if redefault_month < probation_complete_month else 2
print("Month {} re-default linked to Episode {}".format(redefault_month, episode))

def pv(cashflows, annual_rate=0.05):
    return sum(amount / pow(1 + annual_rate, year) for amount, year in cashflows)

episodes = [
    {"name": "Episode 1", "ead": 10000, "recoveries": [(3000, 1), (2500, 2), (1500, 3)], "costs": [(200, 1), (150, 2)]},
    {"name": "Episode 2", "ead": 4000, "recoveries": [(1600, 1)], "costs": [(100, 1)]},
]
for item in episodes:
    pv_recoveries = pv(item["recoveries"])
    pv_costs = pv(item["costs"])
    lgd = (item["ead"] + pv_costs - pv_recoveries) / item["ead"]
    print("{}: PV recoveries={:,.2f} USD; PV costs={:,.2f} USD; illustrative LGD={:.2%}".format(item["name"], pv_recoveries, pv_costs, lgd))

Output

Run the code to see the output.

Runtime loads only after you press Run. Code runs in a separate browser worker with a 10-second limit and a 20,000-character output cap. Use synthetic examples only; browser execution is not a secure environment for secrets or sensitive records.

Runnable Python · local in your browser

Discount recovery cash flows for workout LGD

Discount synthetic recoveries and costs to default date and calculate a capped loss fraction.

Editable Python example

Output

Run the code to see the output.

Assumptions

The selected method represents the product, collateral, seniority, and observation horizon of interest.

Limitations

Market prices may include liquidity, risk premia, and market stress unrelated to a long-run workout path.
Workout samples may be incomplete or slow to mature.

References

Guidelines on PD estimation, LGD estimation and treatment of defaulted assets — European Banking Authority
Basel Framework: IRB approach and credit-risk components — Basel Committee on Banking Supervision
Guidance on credit risk and accounting for expected credit losses — Basel Committee on Banking Supervision

Educational material only. Methods and examples are simplified; state and validate the assumptions required for your use case.