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Spatial financeWhere capital meets hazard, exposure and transition

Statistics for bank risk measurement

Probability and statistics underlie every credit, market and operational risk measure in banking. Default probabilities, loss distributions, value at risk and stress losses all rest on estimation from limited and often unrepresentative data, and the discipline makes the uncertainty in those estimates explicit for the people who rely on them.

01Qeyd

Statistics for bank risk measurement on the record.

Method recordSTEM method
  1. RêbazÎhtîmal û statîstîk
  2. Applied inBanking Nexus
  3. Canonical pageSTEM Commons
02Serlêdan

What probability and statistics does here.

Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In financial stability, capital markets and investment and climate and hazards, statistical methods quantify the likelihood and uncertainty of losses across interconnected pergalan. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on the STEM Commons.

Estimating default and loss parameters

Probability of default, loss given default and exposure at default are estimated from internal and external histories, with margins of conservatism where data are thin or do not cover a downturn.

Operational loss modelling

The frequency and severity of operational losses are modelled from internal, external and scenario data, with heavy tails treated explicitly rather than smoothed away.

Back-testing and calibration

Predicted default rates and value at risk are compared with outcomes using binomial and traffic-light tests, and calibration is reviewed when realised values diverge.

03Stress test

How a shock to assets becomes a system loss.

The equity the system loses after interbank contagion, against the initial hit, as leverage, interconnection and recovery rates change, the core of a macroprudential Testa zextê. Teaching model with stated illustrative values, not a prediction.

Kontrol

Fall in external assets
2.0%
Leverage (assets over equity)
10 times
Interbank share of assets
10%
Recovery on interbank claims
40%
Banks in the system
20
Core banks
20% of banks

Xwendin

Equity lost to the initial shock
18.0%
Equity lost after contagion
45.0%
Added by contagion (DebtRank)
27.0 points
Banks in default
0
Jimar, rêbaz û çavkanî
RoundSystem equity lost (%)Banks in default
018.000
128.800
235.280
339.170
441.500
542.900
643.740
744.240
844.550
944.730
1044.840
1144.900
1244.940
1344.960
1444.980
1544.990
1644.990
1745.000
1845.000
1945.000
2045.000
2145.000
2245.000
2345.000
2445.000
2545.000
2645.000
2745.000
2845.000
2945.000
3045.000

Rêbaz

  1. DebtRank Battiston and others (2012), Scientific Reports 2:541
  2. Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
  3. Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
  4. Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
  5. Pankeya nediyarîyê (Monte Carlo ya parametreyan) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  6. Hesasiyeta yek-yek (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Dane

No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.

How a shock to assets becomes a system loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: DebtRank (Battiston and others (2012), Scientific Reports 2:541); Differential DebtRank (Bardoscia and others (2015), PLOS ONE 10); Contagion in financial networks (Gai and Kapadia (2010), Proc. R. Soc. A 466).

Li seranserê şeş stûnan, bi pêşengiya Nexus Consortium

04Dane

What the method consumes.

Datasets and records used by probability and statistics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DaneRolNîşan
Internal default and recovery historiesParameter estimationÇAVDÊRÎKIRÎ
Rating agency default studiesExternal benchmarkREPORTED
Operational loss databasesFrequency and severity fittingREPORTED
Market price seriesValue at risk and back-testingÇAVDÊRÎKIRÎ
05Exposure, loss and capital

What statistics for bank risk measurement means for exposure, loss and capital.

The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.

Scenario pathways

How statistics for bank risk measurement could be stressed under the scenario families central banks and supervisors use.

OrderlyDisorderlyHot house world202520302035204020452050bilindkêm
Scenario familyTransition riskPhysical riskWhat a stress test asks
OrderlyEarly, gradual policyKêmtirCan the book absorb a steady repricing?
DisorderlyLate, abrupt policyLower to mediumWhat breaks under a sudden repricing and credit migration?
Hot house worldLimited policyHigh and risingWhere do physical losses concentrate in collateral and insurance?
Scenario families as defined by the NGFS; the curves are an illustrative combined pressure index, not NGFS variables or forecasts.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
06Erêkirin

How results are checked.

Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.

Kontrol bikinHow it is done
Out-of-time validationModels are tested on periods not used in estimation, including a stressed period where one is available
Low-default portfolio treatmentWhere defaults are rare, conservative estimators and expert overlays are documented and challenged
Tail diagnosticsFitted distributions are checked in the tail, not only on average fit
Sensitivity reportingKey parameter changes and their effect on capital and provisions are reported to decision-makers

Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.

08Stewardship

Who maintains the method.

Zanista pergalan û modelkirin stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

09Girêdayî

Ev rûpel li ser Konsorsiyûmê te ber bi kû ve dibe.

10Pirs

Pirs û bersiv.

What are the limits of probability and statistics in this field?
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
How are results checked?
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
Çavdêrxaneya canonical method page lists every platform that applies it.
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rêzeya Pergalên Stratejîk is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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