credit risk irb model

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Credit Assessment process

Credit Analysis

• Management quality• Business model• Competitiveness • Key success factors• Proposed activity & financing requirement• Financial history, performance, capital usage and financing strategy• Debt capacity , Cash Flow and Fund Flow analysis• Industry cycles and business risk• Internal Rating and probability of default

Transaction analysis

• Purpose of financing • Determine type of financing - asset /receivable/cash flow

financing/structured• Risk mapping of transaction, is it bankruptcy remote the obligor ? Are there

any risk sharing arrangements ? Co financing, Bank acceptances or Letter of credit backing etc.

• Is the transaction self financing ? i.e. commodity based lending • Extent of collateral risk-collateral value and market risk• Is there a need to obtain additional collateral or balance sheet cash flow is

adequate?• Are there any contingent risks in the transaction which needs to be priced ?• Transaction rating based upon credit support available and risk sharing

Credit Structuring

• Arrive at residual risks which are open in the transaction • Think through the possibility of either sharing or pricing the residual risks• Iterate the RAROC based pricing based on residual risk and transaction

rating ( based upon additional structuring of credit support). In case of treasury based transaction , we can look at market credit support such as liquid collaterals .

• Arrive at the final pricing and transaction rating and residual risks which are open

• Specify the terms and conditions required to govern the transaction• Put up the credit note for committee approval

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Credit Assessment process

Credit Approval

• Obtain credit approval• Communicate /negotiate with client on terms &

financial covenants• Carry out pre-disbursement checks-

documentation and collateral execution• Classify the credit exposure and profile the

exposure details into the credit risk data base• Monitor exposure regularly

Life cycle monitoring

• Carry credit reviews and rating watch both Internal & External rating migration

• Monitor collateral risk• In case of structured transactions such as

securitization track quality of asset pool, collection efficiency and default rates if any

• Revise rating in case of any downgrades or upgrades and review with Credit & Risk committees

• Follow classification as per asset classification norms

• Report and trigger action in case of any impairment

3

Management -Industry strategy performance & business model

• Promoter background• Past ventures• Creditworthiness with banks/fin institutions

Promoter, Management quality & governance

• Five Force analysis of Industry competitiveness local/global• Industrial policy environment• Key success factors that drive performance• Key Industry risks• Past financial performance of Industry

Industry/Competitive Advantage & Key Success factors

• Product /service• Fixed capital Intensive or working capital asset driven business• Distribution driven/relationship driven• Customer types • Impact of competitive forces on business model• Does the business model have the key Success Factors?

Business Model

Porter’s Five Forces

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Financial strategy performance & earnings dynamics

• Product positioning, marketing supply chain & pricing strategy

• Competitive Advantage • Operations strategy

Corporate Strategy

• Financing leverage• Earnings model• Cash flow management• Fund Flow alignment• Long term sustainability • Asset creation for future growth• Enterprise Value creation

Financial Strategy

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Financial strategy performance & earnings dynamics

Working capital strategy Net working capital /

Current assets Aging of debtors

Aging of payables

LiquidityCurrent Ratio

Quick ratioOperating cash flow to sales

Debt repayment capacityDSCR for short term debtDSCR for long term debt

Interest cover ratio

Liquidity and use of capital

Fund flow analysisCash flow analysis

Earnings and profitabilityRevenue-Core income

Other incomeEarnings growth

Capital efficiencyROCEROE

ROCE-Cost of Capital-=spread Stock Beta

Enterprise Value

Benchmarking Bench financial ratios with

mean of rating cohortsBenchmark financial

performance strategy and earnings with industry

peers

Future plans and financial projections analysis

Challenge assumptions for future financial projections

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Credit Risk evaluation & selection of MSME exposure-Using credit scoring model

Qualitative factors

Credit Score

Willingness to pay

Financial Factors credit Score

Ability to pay

More Accurate Model to predict probability of default

Conditioned on business management and other soft factors

Conditioned more on financial factors

Use of credit scoring model is widely applied in MSME segments. For IRB approach, preparedness on this front is a must

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Credit risk -Case study of an MSME unit

Credit risk evaluation of an MSME unit using the broad credit evaluation process & scoring model

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The structure of the BASEL II&III Capital accord

M in im u m ca p ita lre q u ire m e n ts

S u p e rv iso ryR e v ie wP ro ce ss

M a rke tD isc ip lin e

T h re e B a s ic P illa rs

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The structure of the BASEL II&III Capital accord

StandardisedApproach

InternalRatings-based

Approach

Credit risk

BasicIndicatorApproach

StandardisedApproach

AdvancedM easurem entApproaches

O perationalrisk

S tandardisedApproach

M odelsApproach

M arketrisks

R isk weightedassets

CoreCapita l

Supplem entaryCapita l

Defin ition ofcapita l

M in im um capita lrequirem ents

Supervisory reviewprocess

M arketd iscip line

ThreeBasic P illars

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BASEL II & Indian Banks

RBI currently prescribes Standardised Approach for all Indian and foreign banks in India

Indian Banks & Foreign Banks in India follow standardised approach for regulatory capital

Credit Risk + Market Risk +Operational Risk=RWACapital adequacy @9% of RWABanks parallel run for IRB foundation targeted by

2012

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IRB ( Internal Ratings Based) Approach

IRB approach fundamentally has two sub approachesFoundation approachAdvanced approach Both approaches are based upon a single factor

default mode modelThe model is also called the asymptotic single risk

factor modelIRB is adapted from Merton & Vasciek single asset

model to credit portfolios

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IRB Approach

IRB method of risk capital measurement is based uponInternal ratings of bank credit exposuresUnexpected losses ( UL)Expected losses (EL)IRB Risk weight function equation or model produces

capital for the ULExpected losses (EL) are to be treated separatelyUnexpected losses (UL) concept relies on the Value at

Risk concept as its foundation

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Economic foundations of IRB model

Credit Loss rate over time –normal business ( EL) & abnormal losses ( UL)The credit losses for a Bank over time vary depending upon the severity of loss rate irrespective of the portfolio to which the loans belong

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Economic foundations of IRB modelLoss distribution and Value at Risk. The potential losses at low frequency are higher in severity by historical experience. The IRB model targets in measuring credit risk capital for such potential unexpected losses. Expected losses are to be priced & provided for. In an extreme event, there is a likelihood of an UL of Rs 200 crs. 5% of the time (meaning 95% confidence level) over an horizon of one year. In other words likelihood that the bank will remain solvent over one year horizon is 95%

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Economic foundations of IRB model

The three basic parameters Probability of default ( PD) Loss Given Default (LGD) = (1-Recovery% on default) Exposure at Default (EAD) All the above three parameters are on default model only IRB analytical model is Portfolio invariant IRB analytical model in ratings based at individual loan asset level EL=PD*LGD*EAD EL is based upon average PD rates PD is expressed in %, LGD in % & EAD in value terms EL= 0.05%*35%*452 crs. =0.0791 crs.

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Asymptotic Single Risk Factor model (ASRF)-IRB

IRB analytical model is based upon the following postulates

Portfolio invarianceLaw of large numbers Idiosyncratic risk cancel out each other & only systematic

risk impact loss rates in large portfoliosGranularity of portfolios is largeAnalytical model uses conditional PD and downturn LGD

for estimation of ULFor IRB framework target VAR =UL +EL=Capital +Provisions

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Risk Components: Drivers of Credit Risk

Driver of

Credit Risk

Standardised

Approach

IRB

Approach

Obligor risk Credit assessment institutions

Probability of Default (PD)

Transaction risk Credit risk mitigation techniques

Loss Given Default (LGD)

Likely size of exposure

Credit conversion factors

Exposure at Default (EAD)

Maturity Limited recognition Maturity (M)

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Asymptotic Single Risk Factor model (ASRF)-IRB-Systematic risk factor

The IRB analytical model can be understood in its different components The model conditions the average default rate to arrive at the value of systematic risk factor that influences the credit loss

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-3.84170562

-3.577063509

-3.312421398

-3.047779286

-2.783137175

-2.518495063

-2.253852952

-1.989210841

-1.724568729

-1.459926618

-1.195284507

-0.930642395

-0.666000284

-0.401358172

-0.136716061

0.12792605

0.392568162

0.657210273

0.921852384

1.186494496

1.451136607

1.715778719

1.98042083

2.245062941

2.509705053

2.774347164

3.038989275

3.303631387

3.568273498

3.8329156090

50

100

150

200

250

300

350

Normal distribution of systematic risk factor values

Frequency

continuous systematic risk factor values

Freq

uenc

y

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Asymptotic Single Risk Factor model (ASRF)-IRB LGD

LGD measures for the IRB model should be based either on supervisory estimates or internal bank historical data. Under foundation IRB method, downturn LGD will be provided by the regulator and in case of IRB advanced model, the same needs to be estimated by the Bank and used in the model

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Asymptotic Single Risk Factor model (ASRF)-IRB –Expected Loss

The IRB –ASRF model computes the capital which is the entire figure from the origin to the VaR. However this also includes the loss during normal business conditions. Therefore Expected losses will need to be deducted in the IRB model to arrive at only the unexpected loss. The EL is defined as average PD *LGD

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For performing loans in IRB model, LGD is the downturn LGD which is higher than LGD during normal business condition

In of non performing loans technically the terms N and PD would be 1 and the model output would be equal to 0.

However still a capital charge to take care of uncertainty in recovery has to be made

For default assets, best estimate of EL and LGD will be compared and the difference will be taken as Unexpected loss or capital charge

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Asymptotic Single Risk Factor model (ASRF)-IRB –Asset correlations

Volatility of EL and UL over time is due strong correlation among individual exposures and correlation with a single systematic factor. The IRB model lays more stress on single systematic risk factor i.e. the state of global/national economy . In the IRB model asset correlation is the correlation of the asset default with the systematic risk factor. As all obligors are exposed to the single systematic risk factor, in aggregate the portfolio too is exposed to the single factor on portfolio defaults

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Asymptotic Single Risk Factor model (ASRF)-IRB –asset correlations

Empirically & by intuition it is found... Increase in PD, the idiosyncratic risk of individual borrowers

increases As PD ( default rates) among individual asset class increase,

the correlation effects with other borrowers decrease Individual default rates depend less on systematic risk factor Correlations are effected by size of firms Higher the size of firm, higher the dependency on overall

economy and hence correlation increases Example : present financial crisis, large banks default lead to

contagion effect Asset correlations also increase by asset size There is a size

adjustment in the IRB model

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Asset correlations and default rates are related through an exponential function within limiting limits of 0 to 100% default rates. For very high PD, correlation limits are 12% for very high PD and 24% for very low PD.

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Correlation adjustments in the IRB model .Correlation is an input provided in the model which has to be estimated for asset class The size adjustment for borrowers having annual sales between Rs 35 crs. to 350 Crs. ( taken at 1 Euro of Rs 70)

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Correlation adjustments in the IRB model .Correlation is an input provided in the model which has to be estimated for asset class

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Asymptotic Single Risk Factor model (ASRF)-IRB –Maturity factor

Empirical evidence states...Long term credits riskier than short term creditsExposures with higher maturity and low PD have greater

rating /credit quality migration RiskMigration risk lower for exposures with high PD as there

these exposures are closer to defaultMTM >given low PD , higher maturity MTM <given high PD, lower maturityOverall maturity effect dependent upon PDIRB for the above reasons includes maturity effect

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Asymptotic Single Risk Factor model (ASRF)-IRB –Maturity factor

The maturity adjustment for the IRB equation is linear with a steep slope as at higher PD levels, the effect of maturity is lower . The factor B in the model is the regression slope of M with PD

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Asymptotic Single Risk Factor model (ASRF)-IRB –Confidence Level

Confidence level in IRB foundation approach is a supervisory inputCurrently 99.99% i.e. 0.010% . The idea of BASEL II being that at least a solvent large bank would be of AA rating cohort

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Asymptotic Single Risk Factor model (ASRF)-IRB –Risk Capital

Comprehensive view of the Capital Charge computation .

RWA on every single exposure is arrived at by multiplying 12.5 * K * EAD. The scaling factor to give effect to UL is 12.5 and is arrived at by the reciprocal of 1/8 where 8% is BASEL minimum capital adequacy. Capital charge will then be 8% ( 9%) for India on the computed RWA

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Collateral effects-IRB capital charge

IRB recognizes credit risk mitigation

Collateral should be eligible as per regulatory requirement

Eligibility of collateral same as standardised approach

Effective LGD to be applied on Exposure after risk mitigation

Minimum LGD as per foundation is 45% for unsecured senior corporate loans

LGD for unsecured subordinated loans is 75%

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Collateral effects & Credit Risk Mitigation –IRB approach

No correlation between credit quality of counterparty with collateral

Where collateral is held by custodian ( in case of commodities, securities etc.) co mingling of assets to be avoided

Provide haircuts on collateral value based upon market risk Internal estimates of collateral market risk or supervisory estimate

of haircut can be used

Effective LGD= (E*/E) * LGD

E* = effective exposure calculated after considering credit risk mitigation

E=exposure without considering collateral based risk reduction

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No correlation between collateral & counterparty

Where collateral is held by custodian ( in case of commodities, securities etc..) co mingling of assets to be avoided

Provide haircuts on collateral value based upon market risk

Internal estimates of collateral market risk or supervisory estimate of haircut can be used

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In IRB approach the bank should estimate effective exposure

E* = max(0,{E*(1+He)-C*(1-Hc-Hfx) He= Increment to exposure due to market movements Hc= Haircut to collateral due to market risk Hfx =Haircut to exposure due to foreign exchange

movements Effective LGD= (E*/E) * LGD E* = effective exposure calculated after considering credit

risk mitigation E=exposure without considering collateral based risk

reduction

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Default Probability Modeldevelopment

PD estimation requires a Internal rating and a quantitative approach to rating data

Internal rating system must have two dimensionsRisk of default =f(M,B,F,I)parametersTransaction specific risk factors ( facility rating)Risk default must me measured by assigning

minimum rating grade distinctions to avoid risk concentrations + or – signs within each grade can be used

Rating grades and PD estimated should have an exponential relationship

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Internal ratings model

Facility rating or the transaction element must reflect the Loss severity consideration

Facility rating should influence overall rating through effects of

Seniority of the exposureProduct typeCollateralCollateral market riskConcentration risk through collateralFacility rating should enable estimate of LGD

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Internal ratings model

Banks should test for rating consistency Deviation of financial parameters/credit

scores and PD ( actual default rates) or model PD rates across rating grades should be observed/minimised

Banks should harvest minimum 5 year historical data on rating cohorts

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Credit Risk & Analysis Internal ratings

Source : Credit Risk Assessment guidelines-ONEB paper

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The Credit Management Process

Pre-Assessment

Pricing

Reject

Internal Credit Rating

CorrelationsLGDPD

CR Measurement

CR Management

Loan work outsProvisioning Capital Allocn.

Accept

EAD

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Traditional Approach to PDs

• Focus on historical accounting data• Purely empirical approach uses historical default rates of

different credit ratings (e.g. Moody’s and S&P’s) • The traditional modeling approach attempts to identify

the characteristics of defaulting firms • First serious attempt usually attributed to Altman (late

‘60s) who used Discriminant analysis (Z scores)• Scoring models have stood up well over time and are still

used - especially in low-value, high-volume lending• Later models have used Logit, Probit and ANNs

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Default probability models

MSME portfolios are generally granular Relatively higher degree of homogeneity Estimate of pool level PD can be used initially Credit scoring or logistic regression models suit better for

PD estimation in MSME segmentBased on few financial ratios which are key

risk indicators and easy to monitor The above models are also easy to validate Altman Z score model methodology is the

basic foundation of such PD modeling

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ALTMAN Z SCORE MODELS

The Altman Z score Model uses the financial ratios of default & non default firms to estimate credit scores. Variant of Altman credit risk scoring models are based upon finer statistical regressions The model construct 0.012 (X1)+0.041(X2)+0.033(X3)+0.006(X4)+0.999(X5) X1=Net working capital/TOA X2=Retained Earnings/TOA X3=EBIDTA/TOA X4=Market Value of Equity/TOA X5=Sales/TOA Z=overall credit Index Interpretation of Z Index values for credit quality

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ALTMAN Z SCORE MODELS

The Altman Z score Model uses the financial ratios of default & non default firms to estimate credit scores. Variant of Altman credit risk scoring models are based upon finer statistical regressions

Z=overall credit Index Interpretation of Z Index values for credit quality Critical cutoff score is 1.81. Loans having credit score below 1.81 are in higher

probability of default risk Loans between 1.81 to 2.50 are in the mid category Loan exposures more than 2.50 are in the strong credit

quality class

CRISIL –PD estimates

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CRISIL default study estimates PD for period 1998-2010

The method followed is static pool method year on year

PD estimated for both the Long term and short term debt issuances


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CRISIL PD estimates are for one year transition as required by IRB foundation. The below estimates are for mix of mid size and large Corporate units


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CRISIL PD estimates are for one year transition of short term issuances. The below estimates are for mix of mid size and large Corporate units

CRISIL –Industry wise default rates

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Industry segment wise default rates worked out by CRISIL on its rated pool

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Credit Migrations-MTM approachConcept of MTM approach probability credit migrations to default status. The MTM approach is continuous and not a discrete approach such as default mode

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Portfolio Credit Risk Modelling While the term “credit risk model” is applied loosely to cover all

forms of statistical analysis, including the estimation of PDs, credit risk modelling in the true sense of the term involves the portfolio assessment of credit risks and the use of the model as the framework for managing credit risk within the bank

There two fundamentally distinct portfolio modelling approaches :

Default mode modelling, and Mark-to-market modelling

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Mark-to-Market Modelling

MTM models define credit events to encompass both default and migration of credit ratings

By valuing every credit in every possible state and then probability weighting them, the MTM model effectively simulates the price at which any credit could be sold - hence the MTM label

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Default Mode Modelling

Default mode accounts for default event only

The default no default event is statistically modelled as a binomial state

DM method is based upon the binomial distribution and the assumption that the credit portfolio is granular

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Credit Risk portfolio level –default mode

EAD ( exposure at default) = Principal committed + Accrued interest

EL( expected loss) =EAD * PD * LGD

LGD= (1-Recovery %)*EAD

Unexpected Loss is the deviation about EL

UL will be maximum for a portfolio of credit assets in case correlation is 1

Due to diversification UL reduces

EL will be recovered through risk based pricing

UL- EL = economic capital

EADLGDPPUL )1(

Framework of RBPM

Setting of Risk Appetite & Limits

Estimation of Risks

Integration of Risks

Stress Testing

Capital Computation

Capital Allocation

Estimation of Cost of Capital

Allocation of financial Revenue & Expenses

Allocation & apportionment of non-financial expenses

Computation of Risk Based Performance

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Risk Adjusted Return on Capital

A RAROC model links Risk and Economic capital (Not regulatory capital)

Economic capital is the capital needed to absorb unexpected losses

Expected losses are priced into the transaction

So RAROC = Risk Adjusted Return over economic capital

Risk Adjusted Return= Return- Transaction costs ( variable & semi fixed + proportion of fixed cost allocated to product line) - Cost of Risk

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Risk Costs = Expected loss

RAROC = Risk adjusted return ( RAR) / Economic capital in % terms

RAROC is similar to ROCE but with a risk adjustment in it

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RAROC = Risk adjusted return ( RAR) / Economic capital in % terms

RAROC is similar to ROCE but with a risk adjustment in it

Overall spread = RAROC – Cost of Capital (Tier I +Tier II)

Benchmark spread to peer groups

Compare RAROC across product lines /business verticals

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Measurement of Economic Capital

Economic capital = Unexpected Losses

Credit VaR Market Risk- VaR Operational VaR E C

•In modelling Economic capital correlation is set to 1 for conservative basis or based on observed correlation data

•Stress tests can involve correlation inputs which observed under stressed

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PD& PD Migration

Obligor Risk RatingProcess

L G D[Loss Given Default]

StructureAsset Quality

EA D[Expos Given Default]

StructureTerm

Loan Type

Expected Losses -EL U L[Unexpected Loss]

Expected Loss individual exposure

Correlation effects

Portfolio Capital Model

EC(Economic

Capital)

Total RevenuesInterest + fee income

-Overhead & other variable /semi fixed costs

- Expected Loss

- Income Tax & Capital Tax

NIX(Non-Interest

Expense)

L G DAmountPD

NetIncome

R A R O C

RAROC MODEL INPUT COMPONENTS

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Total Revenue – Overhead - Expected Loss - Taxes RAROC =

Marginal Credit Capital where,

Time Horizen = One year forward estimate of profitability

Total Revenue = Expected 1st yr Spread Revenue + Upfront Fees

Overhead = Non-Interest Expense (fixed charge applied per segment)

Expected Loss = Obligor PD * Facility LGD * Loan Exposure

Taxes = Jurisdiction specific tax payable on loan income

Marginal Credit Capital = 1st yr credit capital based on one factor VaR model (similiar to Basel II)

on

RAROC EQUATION

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IRB & RAROC Model-EXCEL examples

EXCEL based examples on the IRB credit Risk equation and RAROC pricing of MSME loan exposure will be practiced

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Other salient components and key assumptions of model:

•RAROC output compared to Hurdle Rate (cost of capital + risk premium) for all new/revised deals

•RAROC integral but not sole factor in lending decision, however, any deal falling below Hurdle requires ‘level-up’ sign-off with rationale

•Data Inputs: PD consistent with obligor RR; consistent with ‘through the cycle’ outlook LGD facility specific and reflects ‘average’ loss expected EAD based on expected utilization plus ‘add-on’ factor Marginal Capital estimated using one factor model. Capital influenced by PD, LGD, and

Term of deal. A Default/No Default model with loose assumptions on portfolio granularity and correlation/diversification

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Foreign funding options for MSME

Methods of raising foreign funds

External Commercial Borrowing methods Bank loans, Buyers credit, Suppliers credit Securitized instruments (floating rate note, fixed rate notes,

partially ,optionally convertible preference shares with a minimum maturity of 3 years)

Foreign Currency Convertible Bonds (FCCB) Foreign Currency Exchange Bond (FCEB)

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Foreign funding options for MSME Basic compliance requirements & policy guidelines The ECB bank borrowing methods will be guided by the ECB

circular of RBI The fund raising options such as FCCB, FCEB, Preference

shares( partially/optionally convertible) are guided by FEMA and ECB guidelines and specific scheme guidelines issued by Ministry of Finance Govt of India.

Thank You

credit risk irb model

Documents

key success

basel iiamp

systematic

credit support

residual risks

expected losses

business model

irb model