Stochastic credit RAROC

Load a loan tape and simulate the book one year at a time. Exposure, probability of default and loss given default are all random, costs are charged, capital is read off the simulated loss distribution, and RAROC comes out as a distribution you can cut by any column in the file.

1 · Loan tape

One row per credit. CSV, semicolon or tab-separated, including a block pasted straight from Excel. The file stays in your browser.

Demo portfolio · 5,000 loans · balance 23,160,354 · 14 columns

Column mapping

Guessed from the headers. Balance is required; PD comes from a score or a PD column, LGD from a guarantee type or an LGD column.

Balance (EAD) *Interest rateScore / delinquency statePDGuarantee typeLGDCurrencyFunding rateFeesLoan ID

Dimensions to report by

Every column switched on here becomes a breakdown in the results.

2 · Loan-level risk inputs

How each row gets its PD, its LGD and its cost of funds.

Probability of default

From the score, via a transition matrixFrom a PD column

One-year transition probabilities between states. Each row is normalised to 1; the last state is default. PD of a state = its column to default after the horizon.

Horizon years

from \ to	1	2	3	4	5	6	PD
1 · CURRENT	%	%	%	%	%	%	2.91%
2 · DELAY 1-30 DAYS	%	%	%	%	%	%	7.04%
3 · DELAY 31-60 DAYS	%	%	%	%	%	%	11.77%
4 · DELAY 61-90 DAYS	%	%	%	%	%	%	19.56%
5 · DELAY +90 DAYS	%	%	%	%	%	%	24.17%
6 · DEFAULTED	%	%	%	%	%	%	100.00%

Loss given default

By guarantee type, PERT(min, most likely, max)From an LGD column

The expected LGD of a PERT is (min + 4·most likely + max) / 6, not the most likely value.

Min	Most likely	Max	Mean
%	%	%	89.8%
%	%	%	27.5%
%	%	%	38.3%
%	%	%	45.8%

LGD for types not in the table %

Cost of funds

Rate by currencyOne flat rateFrom a column

Currency	Rate
	%
	%

Flat funding rate%Liabilities with cost / portfolio%The share of each balance funded by paid liabilities; the rest is equity or free funds.

3 · Systematic risk by segment

The part of the risk that does not diversify away. Each segment’s factors scale every loan in it; their means are 1, so expected PD and LGD stay what the tape says.

Risk segments

Segment	loans	balance	PD	LGD	Parameters	PD volatility (CV)	LGD volatility (sd)	Beta(α, β) PD · LGD
CORPORATE	227	12.7M	5.46%	67.8%	CV %	%	%	1.1, 19.3 · 14.1, 6.7
SME	756	4.1M	8.63%	54.2%	CV %	%	%	1.5, 16.3 · 20.5, 17.3
MORTGAGE	633	3.6M	4.85%	27.5%	CV %	%	%	3.8, 73.7 · 15.0, 39.4
VEHICLES	865	1.6M	5.97%	38.3%	min % max %	%	%	3.7, 58.3 · 18.1, 29.1
CONSUMER	1,514	931.3K	7.32%	87.7%	min % max %	%	%	3.0, 37.9 · 37.0, 5.2
CREDIT CARD	1,005	272.7K	8.05%	89.8%	min % max %	%	%	2.5, 28.3 · 31.9, 3.6

Correlations (Spearman)

Between the segment factors. Cross-segment coefficients are the within-segment ones times the macro coefficient, so the matrix is always consistent.

Correlate factorsEAD – PDEAD – LGDPD – LGDAcross segments (macro)

4 · Income, costs and capital

Operating costs are charged per loan; capital is either simulated or a fixed ratio.

Fixed operating cost (per year)Uncertainty (min / max as % of it)%%Allocate fixed costVariable operating cost (per year)Charged in proportion to drawn balance: 0.600% of balance.Servicing cost per loanInterest lost on a defaulted loan%0% as in the workbook; 50% if defaults happen mid-year on average.Return on capital (risk-free)%Tax rate%

Capital for RAROC

Economic capital (simulated: loss at confidence − EL)Capital ratio × balance (workbook)

Confidence levelHurdle rate (RAROC target)%

5 · Simulation

IterationsSamplingSeed

Run the simulation to see results.

Stochastic credit RAROC

1 · Loan tapeⓘ

Column mappingⓘ

Dimensions to report byⓘ