credit rating models 4 banks

8/2/2019 Credit Rating Models 4 Banks

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Credit Rating Models and Their Applications

Dr. Arindam Bandyopadhyay

National Institute of Bank Management



• US Federal Reserve Chairman Mr. Alan Greenspan inApril 2004 had made an interesting comment before theSenate Committee:

• “Only through steady and continued progress inmeasuring and understanding risk will our bankinginstitutions remain vibrant, healthy and competitive inmeeting the growing financial demands of the nation.

Therefore, the regulatory authorities must provide theindustry with proper incentives to invest in riskmanagement systems that are necessary to competesuccessfully in an increasingly competitive and efficientglobal market.”

• Risk management is, first and foremost, a science. If youuse accurate data, reliable financial models and the bestanalytical tools - you can minimize risk and make the oddswork in your favour.



The Focus: Credit Risk

A bank always faces the risk that some of its borrowers may

renege on their promises for timely repayments of loan,

interest on loan or meet the other terms of contract. This

risk is called credit risk.

Credit risk is the risk that the borrower may be unable orunwilling to honour his obligations under the terms of

contract for credit. A major part of the asset of a bank

consists of loan portfolio. Banks suffer maximum loss due to

non performing assets. The credit risk is thus a dominant

concern on management of asset portfolio of any bank.

credit risk varies from borrower to borrower depending on

their credit quality. Basel II requires banks to accurately

measure credit risk to hold sufficient capital to cover it.



Importance of Credit Risk Models

• Credit risk models are intended to aid banks inquantifying, aggregating and managing risk acrossgeographical and product lines.

•

The outputs of these models also play increasinglyimportant roles in banks’ risk management andperformance measurement processes, customerprofitability analysis, risk based pricing, active portfoliomanagement and capital structure decisions.

• Basel II has made a real contribution by motivating anenormous amount of effort on the part of banks (andregulators) to build (evaluate) credit risk models thatinvolve scoring techniques, default and loss estimates, and

portfolio approaches to credit risk problem.



What is Credit Risk?

Credit risk is risk resulting from uncertainty in acounterparty’s ability or willingness to meet its contractual

obligations.

Credit risk is the probability of losses associated with

changes in the credit quality or borrowers or

counterparties.

These losses could arise due to outright default by

counterparties or deterioration in credit quality.

If credit can be defined as “nothing but the expectation of a

sum of money within some limited time”, then credit risk is

“the chance that expectation will not be met”.



Key Elements of Credit Risk

Factors affecting credit risk (expected and unexpected losses

arising out of adverse credit events) Exposure at Default (EAD)

Probability of Default (PD)

Loss Given Default (LGD)

Default Correlations

Estimation of the average (mean expected) losses due to credit iscommonly used to:

1) set reserve requirements for doubtful accounts;

2) establish minimum pricing levels at which new creditexposures to an obligor may be undertaken;

3) price credit risky instruments such as corporate bonds orcredit default swaps; and

4) calculate risk adjusted performance measures such as

RAROC.



The bank can also suffer losses in excess of expected losses,say, during economic downturns. These losses are called

unexpected losses.

The capital base is required to absorb the unexpected

losses, as and when they arise.

Combining the results of models for default probabilities,

recovery rates, and default correlation, a risk manager can

estimate the expected and unexpected credit losses of an

instrument and/or a portfolio, given knowledge of the

exposure amount

Key Elements of Credit Risk



Inputs of Individual Credit Risk

In assessing credit risk from a single counterparty, the Bank must consider the

following inputs : Exposure at Default (EAD): In the event of default, how large will be the

outstanding obligations if the default takes place. EAD gives an estimate of

the amount outstanding (drawn amount plus likely future drawdowns of yet

undrawn lines) in case the borrower defaults.

Probability of Default (PD): The probability that the obligator or

counterparty will default on its contractual obligations to repay its debt. PD

per rating grade is the average percentage of obligors that default in this

rating grade in the course of one year.

Loss Given Default (LGD): The percentage of exposure the bank might lose

in case the borrower defaults. Usually it is taken as: 1-recovery rate

Credit Migration: Short of a default, the extent to which the credit quality of

the obligator or counterparty improves or deteriorates.

Maturity: Time Horizon (Usually a time horizon of 1 year is considered).



1st Stage Output (at individual asset level)

Expected Loss for the ith advance (ELi) = PDi * LGDi *

EADi

Unexpected Loss (ULi) is the unanticipated loss in the risky

asset due to the occurrence of default or unexpected credit

migration

ULi is defined as the standard deviation of the value of the

asset

ULi =EADi*LGDi*STDPDi

=EADi*LGDi*SQRT {PD*(1-PD)}

Assumptions

Loss Given Default has no uncertainty

Loss Given Default and Expected Default Probability are

independent



Definition of Default/NPA

In the Indian parlance the defaulted loan is titled as NonPerforming Asset (NPA). A loan is defined NPA on which

the interest or installment of principal has remained past

due for a specific period of time.

The specific period of time for the year 1993 was four

quarters, for 1994 three quarters, 1995 two quarters.

Subsequently the RBI has implemented the 90 daysdelinquency norm with effect from 31st March 2004

following the BIS norms.



Data Required for Credit Risk Estimation

The data required for credit risk estimation are essentially the

ones used by the banks for appraisal of credit proposals. These

include:

1) Detailed data on income and expenditure, assets and liabilities, cash

flows of borrower, data on product structure, loss / claim history.

2) Market price of debt and equity instruments of quoted companies.

3) Credit ratings assigned by rating agencies

4) Credit derivative price data

5) Industry, market share data

6) Data on Management quality The gathering of data for credit risk is a challenging task. IT

system should be built in such a way that data on different aspects

of operation of a loan flows into a central database that can be

used for credit risk analysis.



The First Dimension of Basel II:

Estimating Probability of Default

As the basic purpose of analysis of credit risk as part of

Basel II is to provide for adequate capital as a safety net

against possible default, the first step is to develop a

method of quantifying the chance of default. Thus, it is the

frequency of default and the regularity with which it

occurs that matters.



Approaches for Default Prediction

Credit Scoring Systems

Expert judgement based Rating Models

Structural Models/Option Theoretic Models/

Market based Models



Why Ratings Matter?

• To ascertain financial health of individual obligor,facilities and portfolios and thereby assist in lendingdecisions.

• Ratings allow to measure credit risk , and to manageconsistently a bank’s credit portfolio, i.e., to alter thebank’s exposure with respect to type of risk.

• Ratings are useful for pricing of a bond or a loan withrespect to type of risk.

• Allocate reserve (covered by EL) and capital (covered by

UL)



Credit Rating/Credit Score

An opinion on the inherent credit quality of a company and or the credit instrument.

A Two-Tier Rating System Obligor Rating-indicates the chance of the

borrower as a legal entity defaulting

Facility Rating-indicates the loss of principal and/or interest on that facility;specific to the advance cum collateral



What is Credit Scoring?

• Assigning a numeric formula to arrive at asummary number which aggregates all the risks of default related to a particular borrower.

• The final score is a relative indicator of aparticular outcome (most often, creditworthinessor default probability of a borrower).

• However, as of now, credit-scoring models are notlimited to predicting credit worthiness but alsoused in predicting potential bankruptcy.



External Rating Vs. Internal Rating

• External ratings are generated by rating agencies(ECAIs).

• ECAIs specialize in the production of ratinginformation about corporate or sovereign

borrowers, they do not engage in the underwritingof these risks. The rating information is madepublic, while the rating process itself remains non-disclosed.

•

Internal ratings, in contrast, are produced byBanks to evaluate the risks they take into theirown books. It is not made public because of havingcompetitive advantage.



Credit Scoring Systems

Credit Scoring systems use the Discriminant Methodology

which determines

which variables characterize “good” firms from “bad”

What weight should be assigned to these variables to

achieve the highest rate of predictive power

The methodology is based on statistical distillation of the

historical data

The final score can be used to discriminate between good

and bad credits Examples: Linear Probability Model, Logit Model, Probit

Model, Discriminant Analysis Model (Altman’s Z Score).



Statistical Approaches to Credit

Scoring Models Linear probability model:

is based on linear regression, and uses a number of accounting variables to predict default probability.

Logit model:

assumes default probability is logistically distributed, and

applies accounting variables as well as non-financialfactors to predict default probability.

Probit Model:

is similar to logit model, except that the probit model

arises from assuming that the probability distribution isnormally distributed.

Discriminant analysis model (e.g., Z-score):

is based on finding a linear function of accounting andmarket based variables that best discriminates between

firms that fail and those that do not.



Linear Probability Model

Regression model with binary dependent variable

(=1 if default occurs and =0 otherwise)

error X ij

n

j

ji Z 1

Example:

Suppose there were two factors influencing the past default

behavior of borrowers: the leverage or D/E and the sales/assets

ratio (S/A). Based on past default (repayment) experience, the

linear probability model is estimated as:

•The major problem is that the estimated probabilities can

lie outside the admissible range (0,1).

iiiAS E D Z ) / (99.0) / (5.0



Logit /Probit Model

The Logit model solves the problem of the unbounded

dependent variable by transforming the probabilities as

follows:

The left hand side is the logistically transformed value of

Z. The Probit Model is an extension of Logit which

considers a cumulative normal distribution rather than a

logistic function.

Both logit and probit models are very close and rarely

lead to different qualitative conclusions, so that it is

difficult to distinguish between them statistically.

i Z ie

Z F

1

1)(

MDA A l i E l Alt ’ Z



MDA Analysis: Example Altman’s Z

Score Altman, for the first time, applied Multiple Discriminant

Analysis (MDA) in response to shortcomings of traditional

univariate financial ratio analysis.

MDA models are developed in the following steps :

Establish a sample of two mutually exclusive groups: firms whichhave “failed” and those which are still continuing to trade

successfully

Collect financial ratios for each of these companies belonging to both

of these groups

Identify financial ratios which best discriminate between groups (F-

test/ Wilk’s Lambda test).

Establish a Z score based on these ratios.



Altman’s Z-Score Model

Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 0.999X5

Z = The Z score

X1 = Net Working Capital (NWC)/Total Asset (liquidity)

X2 = Retained Earnings/Total Asset (cumulative profitability)

X3 = Profit before Interest and Tax (PBIT)/total assets

(productivity)

X4 = Market Value of Equity/Book value of Liabilities

(movement in the asset value)

X5 = Sales/Total Assets (sales generating ability)



Altman’s Z-Score Model It is a classificatory model for corporate customers

Zscore > 2.99 - firm is in a good shape

2.99>Zscore>1.81 - warning signal

1.81>Zscore-big trouble; firm could be heading towardsbankruptcy

Therefore, the greater a firm’s distress potential, thelower its discriminant score

Z-score model can be used as a default probability predictormodel

One of the most frequently asked question is “How did hedetermine the coefficients or weights?”

Altman answers: “The weights are objectively determinedby the computer algorithm and not by the analyst. As such

they will be different if the sample changes or if newvariables are utilized.”



Applying the Z-Score to Predict

Default of an Indian Company

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1990 1992 1994 1996 1998 2000 2002 2004

Z

Altman-Z Score for Arunoday Mills Ltd.

Bankrupted in yr. 2004



Expert Judgement Based Systems

The Risk Rating Methodology Relies on expert’s insights into the borrower’s financial

health.

Encompass financial, industry, business & managementrisk

Projections and Sensitivity analysis

Specify cut-off standards

Separate rating frameworks for large/mid corporates,

small borrowers, retail loans, NBFCs etc.



Risk Assessment Tools for

Large and Medium Businesses

Expert Judgement Based Models (Specific Inputs towards

lending decisions) Risk Categories

InternalExternal

ManagementRiskBusinessRiskFinancialRisk

Industry Risk

FacilityRisk

Example: S&P, Moodys, Banks, NIBM’s Credit Rating Model

E ti ti f PD th h R ti



Estimation of PD through Rating

Transitions

PD can be estimated by analyzing Rating Transitions overtime

The methodology requires a rating wise cohort mortality rateanalysis of the bank’s own internal rating data (say for 5years), to find the number of firms in each rating class in

each cohort moving towards default category (D) The year-wise PDs for different rating grades can be

estimated by counting the number of defaulting companies ina yearly transition and dividing by the total number of firmsat the beginning of the year.

However, in order to obtain a through the cycle stressed PD,one has to take a weighted average of these marginal PDsover the entire sample period (i.e., average of 4 yearlycohorts)



The Transition Matrix

Transition Matrix: Probabilities of credit rating migrating from one rating quality to another, within one year

Table: 3 Average One Year Transition Matrix (Years 1995-96 to 2004-05)

Year 2

Year 1 AAA AA A BBB BB B C DAAA 97.08% 2.92% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

AA 2.54% 87.57% 7.93% 1.05% 0.60% 0.15% 0.00% 0.15%

A 0.00% 4.35% 79.97% 9.14% 3.48% 0.44% 0.73% 1.89%

BBB 0.00% 0.74% 5.90% 67.53% 14.76% 2.21% 3.69% 5.17%

BB 0.00% 0.83% 0.00% 1.65% 57.02% 4.13% 7.44% 28.93%

B 0.00% 0.00% 0.00% 7.41% 0.00% 55.56% 7.41% 29.63%C 0.00% 0.00% 0.00% 2.33% 0.00% 0.00% 51.16% 46.51%

D 0.00% 0.00% 0.31% 0.31% 0.92% 0.00% 0.00% 98.46%

Source: CRISIL long term bond rating



When Do Firms Default? In theory, corporate bankruptcy is driven by the fall in the asset value or

by liquidity shortages (fall in the ability to raise capital to finance

project). The market value of assets is a very powerful default predictor since it is

an indicator of a firm’s economic prosperity or distress

Moreover, some firms default at low asset values despite abundant

liquidity.

The market value of assets at default is on average 65 per cent of the face

value of debt. It varies widely in the cross section, and depends on balance

sheet liquidity, asset volatility and tangibility.

Correct estimation of default probabilities is becoming an increasingly

important element of bank’s measurement and management of creditexposures; inaccurate estimates of EDF could lead to a situation where

too little capital is allocated to risky projects and as a consequence

destroys shareholder value.



Option Theory Approach

Academic belief is that default is driven by

market value of firm’s assets

level of firm’s obligations (or liabilities)

variability in future market value of assets

As the market value of firm’s assets approaches book valueof liabilities, the default risk of firm increases

Default Point: The threshold value of firm’s assets(somewhere between total liabilities & current liabilities) atwhich the firm defaults

Relevant Networth = Mkt. Value of Assets - Default Pt.

Default: Relevant Networth = 0



Steps in Estimating EDFs

Estimation of asset value (current market value)

and volatility of asset return by solving two

Black-Scholes.

Calculation of Distance to Default (DD)

Mapping Expected Default Probability (EDF)from DD



Vardhman Spinning & General Mills Ltd.: Market Net Worth

0.00

1.00

2.00

3.00

4.00

5.00

6.00

2000 2001 2002 2003 2004

Year

R s .

B i l l i o n

DP

MVA



Data and Systems

Basel II demands the creation of historicaldatabases. For PD estimation gathering at least

five years of data is required. For loss given

default (LGD) data and exposure at default

(EAD) data based on internal credit loss

experience, about seven years is required. All this

data must be collated and a 12-month parallel

run staged prior to implementation.



Why Internal Modeling and

Validation is Important?

A bank needs to convince the bank supervisor why

the estimates, PD, LGD and EAD are appropriate.

–

Methodological soundness – Evidence on power of risk differentiation

– Evidence on predictive accuracy

– Benchmarking with external data



Thank You

credit rating models 4 banks

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