Credit losses modeling„Deriving the incurred credit losses using Basel II definitions for the risk components in line with...
Disclaimer         The information presented in here are         the explicit views of the author, and he is         held ...
About this publicationThe CBE has adopted IFRS in year 2008. In specific IAS 39 has a discussion about implementing a mode...
About this publicationAcknowledgements:This publication has been developed by Yahya Kamel of the Financial Services Assura...
Market practice series          Credit losses modeling          Part I „Deriving the incurred credit losses using Basel II...
Content1.List of abbreviations                                          7.Questionable market practices2.Credit losses mod...
List of abbreviationsALL: Allowance for Loan Losses                 Yr: YearPD: Probability of Default                    ...
Credit losses model objectives
Credit losses model objectives1. Crucial for investment decision making process that can be translated into:    a. Credit ...
Credit losses model objectives2. Compliance with financial reporting and regulatory bodies; in terms of the   credit loss ...
Model adoption road map
Model adoption road map1. Determine the Credit losses Model objective.    e.g., to assess the incurred or expected credit ...
CBE new GAAPCredit losses assessment
CBE new GAAP Credit losses assessment    Retail collective ALL should be calculated based on the default rates; that is th...
CBE new GAAP Credit losses assessment    Corporate collective ALL should be calculated based on the default rates; that is...
CBE new GAAP Credit losses assessment     As per page 257 in the new CBE GAAP, the CBE opened the door for other credit ri...
CBE new GAAP Credit losses assessment►   The wide difference in the market practices and confusion about the credit risk  ...
Expected vs. Incurred credit losses
Expected vs. Incurred credit lossesIncurred losses IFRS:► Further, the IASB explains that the accounting model adopted is ...
Credit risk measurement1. Credit exposure segmentation
Credit risk measurement 1. Credit exposure segmentationLoan portfolio segmentation:►    Within the retail asset class cate...
Credit risk measurement 1. Credit exposure segmentation►    The goal of segmentation is to provide meaningful differentiat...
Credit risk measurement 1. Credit exposure segmentation►    Credit process and potential what can go wrong:      Credit as...
Credit risk measurement 1. Credit exposure segmentationRetail portfolio   Branch                        Geography         ...
Credit risk measurement 1. Credit exposure segmentation►      Ultimate retail segmentation could look as below:           ...
Credit risk measurement 1. Credit exposure segmentation►   Ultimate corporate loan segmentation could look as below:      ...
Credit risk measurement2. Exposure At Default “EAD”
Credit risk measurement2. Exposure At Default “EAD”Exposure At Default:► For both the direct and indirect credit exposure;...
Credit risk measurement2. Exposure At Default “EAD”Credit Conversion Factor “CCF”:►   The CCF should differ according to w...
Credit risk measurement 2. Exposure At Default “EAD”Credit Conversion Factor “CCF” (cont‟d):►    Two main methods:1.   The...
Credit risk measurement 2. Exposure At Default “EAD”Illustrative case for the EAD, using cohort method:► ABC construction ...
Credit risk measurement3. Measurement methods (Historical charge-off method)
Credit risk measurement 3. Measurement methods (Historical charge-off method)The graph below represents a loan portfolio o...
Credit risk measurement 3. Measurement methods (Historical charge-off method)      Historical loss rateIllustrative case f...
Credit risk measurement 3. Measurement methods (Historical charge-off method)     Loss Confirmation Period “LCP”►    Loss ...
Credit risk measurement3. Measurement methods (Historical charge-off method)    Loss Confirmation Period “LCP”►   The Effe...
Credit risk measurement3. Measurement methods (Historical charge-off method)        Loss Confirmation Period “LCP”Illustra...
Credit risk measurement3. Measurement methods (Historical charge-off method)    Loss Confirmation Period “LCP”Illustrative...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)The graph below represents a loan portf...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)The risk components can be calculated pe...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)  Credit and recovery event
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Credit and recovery eventCredit (def...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)     Credit and recovery eventIllustrati...
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)     Credit and recovery eventCredit (d...
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)     Credit and recovery eventCredit (d...
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)     Credit and recovery eventCredit (d...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Credit and recovery event►   A credi...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)  Probability of Default “PD”
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)     Probability of Default “PD”Probabi...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Probability of Default “PD”Probabili...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Probability of Default “PD”►   Two m...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Probability of Default “PD”►   If th...
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)     Probability of Default “PD”Illustr...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)     Probability of Default “PD”Illustra...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)     Probability of Default “PD”Illustra...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)  Loss Given Default “LGD”
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)     Loss Given Default “LGD”Loss Given...
Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)     Loss Given Default “LGD”Loss Given...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Loss Given D...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Loss Given D...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Loss Given D...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Loss Given D...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Loss Given D...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)     Loss Given Default “LGD”Illustrativ...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Detailed bas...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)      Loss Given Default “LGD”Illustrati...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)        Loss Given Default “LGD”Illustra...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Types of LGD...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)    Loss Given Default “LGD”Illustrative...
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)  Trade finance
Credit risk measurement3. Measurement methods (Migration analysis- simplex method)     Trade financeBasis of deriving the ...
Credit risk measurement4. Economic and market assessment
Credit risk measurement 4. Economic and market assessmentThe assessed credit losses should reflect the current economiccir...
Credit risk measurement5. Model validation and back-testing
Credit risk measurement5. Model validation and back-testingThe validation and back-testing process is mainly consisted of:...
Credit risk measurement5. Model validation and back-testingBack-testing example►   The Model parameters should be subject ...
Credit risk measurement5. Model validation and back-testingBack-testing example►      The Model parameters should be subje...
Credit risk measurement6. Reference data sets
Credit risk measurement6. Reference data sets►   Data sets: Data that should be tracked and available for the calculation ...
Questionable market practices
Questionable market practices      Loan segmentation                     PD/historical charge-off                         ...
Credit risk documentation
Credit risk documentation Minimum requirements:1. Credit exposure segmentation                       4. Reference data set...
FAQ
FAQIRB risk components (EAD, PD, LGD) calculation:1. How should the sold off loan portfolio impact the risk components?   ...
FAQPD calculation:1. How should the withdrawn ratings be treated?     The „withdrawn ratings‟ is observed when an obligor ...
FAQPD calculation … continued:2. How should the new credit exposure that arrive in the middle of the period be   treated? ...
FAQCCF calculation:►   There are instances when the borrower have settled a portion of the    outstanding loan, resulting ...
FAQLGD calculation:►   There are instances when the LGD is negative or some other instances when    it‟s very highly posit...
FAQSecuritized loan calculation:►    How should the credit losses of the securitized loan portfolio be measured?     It sh...
Data requirements
Data requirementsExample of the data requirements►   Hereby we list an example of the data requirements, subject for use u...
Data requirementsCorporate loan portfolio:1. Direct exposure: Performing & Non-performing (customer ID, name, total   outs...
Data requirementsCorporate loan portfolio:2. Indirect exposure: Performing & Non-performing (customer ID, name, total   ou...
Data requirementsRetail loan portfolio:1. Direct exposure: Performing & Non-performing (customer ID, name, total   outstan...
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Market Practice Series (Credit Losses Modeling)

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The Central Bank of Egypt “CBE” has adopted IFRS in year 2008. In specific IAS 39 has a discussion about implementing a model that can derive the incurred credit losses for a pool of receivables/ loans, which was quite open for market development & practical initiatives.
From the part of the CBE, it has adopted same approach, which led to some wide different market practices, logic, and interpretations, which sometimes have been questionable on a wide scale basis!
So, I've thought to develop some sort of materials that can serve as a practical guidance for quantifying the credit risk, using different simple models, based on Basel II definitions of the risk components.


The intended users of this material are the credit risk professionals who conduct risk analysis, implement risk management policies, or/and are in charge of quantifying the credit risk for a loan portfolio (corporate & retail).
Also, other professionals or officers complying with IFRS, or CBE GAAP.

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Transcript of "Market Practice Series (Credit Losses Modeling)"

  1. 1. Credit losses modeling„Deriving the incurred credit losses using Basel II definitions for the risk components in line with the CBE adopted IAS 39February 26th, 2012 Yahya M.Kamel Financial Services Assurance Banking & capital markets
  2. 2. Disclaimer The information presented in here are the explicit views of the author, and he is held irresponsible for any loss or damage caused by the use of these information.Page 2 Market practice series “Credit losses modeling”
  3. 3. About this publicationThe CBE has adopted IFRS in year 2008. In specific IAS 39 has a discussion about implementing a model that can derive the incurred credit lossesfor a pool of receivables/ loans, which was quite open for market development & practical initiatives.From the part of the CBE, it has adopted same approach, which led to some wide different market practices, logic, and interpretations, whichsometimes have been questionable on a wide scale basis!So, Ive thought to develop some sort of materials that can serve as a practical guidance for quantifying the credit risk, using different simplemodels, based on Basel II definitions of the risk components.The intended users of this material are the credit risk professionals who conduct risk analysis, implement risk management policies, or/and are incharge of quantifying the credit risk for a loan portfolio (corporate & retail).Also, other professionals or officers complying with IFRS, or CBE GAAP.The paper includes interpretative guidance, illustrative cases, and analytical discussions to clarify the practical application of the accountingstandard IAS 39, which is mainly aiming at measuring the credit risk and help answer some of the market doubts & pre-assumptions, like the onesbelow: ► Trade finance incurred losses should bear the same loss rates as for the direct exposure, you don‟t have to, even more credit losses might have been overstated! ► Retail non-performing loans should be fully charged to P&L as a loss, you don‟t have to! ► The complex risk factors, like LCP, CCF, LGD, PD require highly developed systems, otherwise it can‟t be calculated, with few MS Excel skills you can handle it! ► The documentation requirements are unspecific and time-consuming, important & can be short-listed!Scope:IFRS IAS 39, AG 84-92 „adopted by CBE‟; “Collective impairment assessment for a pool of receivables, bearing common credit risk”.Page 3 Market practice series “Credit losses modeling”
  4. 4. About this publicationAcknowledgements:This publication has been developed by Yahya Kamel of the Financial Services Assurance Office at Ernst & Young in Egypt “EY”, with no referenceor co-suggestions with other EY officials; that makes the paper the only explicit views of the author with no legal claims on EY.Suggestions, comments, as well as inquiries regarding credit risk management and quantification from readers of the materials will be muchappreciated.Please feel free to communicate directly with me, Linked-In: http://eg.linkedin.com/pub/yahya-kamel/4b/3b2/565 ► Yahya Kamel Financial Services Assurance Office, Audit senior, Ernst & YoungOther practice publications:Through our market practice series, we have been working within the banking market, figuring out dilemmas, problem accounting matters, andtrying to provide practical guidance that can better assist resolve those problems and give some answers to problem matters.Page 4 Market practice series “Credit losses modeling”
  5. 5. Market practice series Credit losses modeling Part I „Deriving the incurred credit losses using Basel II definitions for the risk components in line with the CBE adopted IAS 39‟ Credit losses modeling Part II “Statistical migration analysis” Credit losses modeling Part III “Structured credit risk models” Developing a valuation technique „Deriving the FV for the inactive debt securities & assessing impairment for the unquoted financial securities in line with the CBE adopted IAS 39‟Page 5 Market practice series “Credit losses modeling”
  6. 6. Content1.List of abbreviations 7.Questionable market practices2.Credit losses model objectives 8.Credit risk model documentation3.Model adoption road map 9.FAQ4.CBE new GAAP Credit losses assessment 10.Data requirements5.Expected Vs. Incurred credit losses6.Credit risk measurement 1. Credit exposure segmentation 2. EAD 3. Measurement methods A. Historical charge-off method B. Migration analysis- simplex method i. Credit and recovery event ii. Probability of Default “PD” iii. Loss Given Default “LGD” 4. Economic and market assessment 5. Model validation and back-testing 6. Reference data setsPage 6 Market practice series “Credit losses modeling”
  7. 7. List of abbreviationsALL: Allowance for Loan Losses Yr: YearPD: Probability of Default IFRS: International Financial Reporting standardsLGD: Loss Given Default IRB: Internal ratings-based approachEAD: Exposure At Default IASB: International Accounting Standard BoardCCF: Credit Conversion Factor LEQ: Loan Equivalent ExposureLEQ: Loan Equivalent Exposure SD: Standard DeviationLCP: Loss Confirmation Period GAAP: Generally Accepted Accounting PrinciplesRR: Risk RatingNPL: Non Performing LoansNPER: Number of PeriodsEM: Effective MaturityEIR: Effective Interest RateCBE: Central Bank of EgyptPV: Present ValueQ: QuarterPage 7 Market practice series “Credit losses modeling”
  8. 8. Credit losses model objectives
  9. 9. Credit losses model objectives1. Crucial for investment decision making process that can be translated into: a. Credit limits in a form of expansion or contraction loan investment policy (geography limit, product, branch, sector limit,.. etc.). b. Potential product opportunities. c. Strengthening the underwriting procedures for certain segment or branch (branch, product, sector, .. etc.) e.g., deteriorated credit risk in certain branch portfolio might be due to fraudulent underwriting, poor underwriting or weak monitoring procedures, which may require higher level scrutiny and more strict underwriting procedures. d. Product pricing to cope up with the increasing credit risk e.g., a deteriorated credit worthiness, would require a decreasing portfolio credit limit and higher interest rate to compensate for the expected higher credit losses.Page 9 Market practice series “Credit losses modeling”
  10. 10. Credit losses model objectives2. Compliance with financial reporting and regulatory bodies; in terms of the credit loss reserves and the capital requirements. It‟s note-worthy that soon or later the CBE will take time to review the basis of calculation of the credit models for the banks operating in Egypt, by then any credit model lacking proper rationale or reasonable risk studies, supported by proper back-testing; might not be approved by the CBE, thus leading to un-liquidation of the credit losses reserved in Equity; whether the credit losses reserves created at the 1st time adoption of new CBE GAAP, or the reserves created for the difference between the old and the new CBE impairment standards.Page 10 Market practice series “Credit losses modeling”
  11. 11. Model adoption road map
  12. 12. Model adoption road map1. Determine the Credit losses Model objective. e.g., to assess the incurred or expected credit losses.2. Determine which credit loss measurement Model to be adopted. e.g., Historical charge-off Model, Migration analysis Model (Statistical „EL‟, Non-statistical „Incurred losses‟), or structured models (Merton‟s Valuation Model KMV, Moody‟s, KPMG‟s Loan Analysis, Credit Metrics, Credit Risk Plus „Mortality rates‟, CPV-Macro, ..etc).3. Determine the Model‟s parameters. e.g., General parameters: Segmentation rule, Credit and recovery event, Periods assessed, Time- horizon, Specific parameters (EAD, PD, LGD).4. Determine the required data. e.g., LGD using the simplex method= 1-Recovery rate, Recovery rate= (Recovered amount or exposure – Costs)/Default exposure.5. Determine the available raw data, and what‟s needed to be developed in the future course of business. e.g., For the LGD, recovered amounts not available, but rating recoveries available.6. Determine the required job staffing, experience, and training.7. Determine the time-table for the Model adoption plan.Page 12 Market practice series “Credit losses modeling”
  13. 13. CBE new GAAPCredit losses assessment
  14. 14. CBE new GAAP Credit losses assessment Retail collective ALL should be calculated based on the default rates; that is the historical average recorded ALL „as per the balance sheet‟ divided by outstanding loans per loan segment. Reference: Default rates: CBE new GAAP, page 240-242, 244, “Collective ALL basis of calculation for the retail loans”, last paragraph. LCP: CBE new GAAP, page 257-258, “Collective ALL basis of calculation”. e.g. a retail loan portfolio at a total value of $1200, as of 12/31/2012, assigned a risk rating of 2 „Bucket 2‟, based on the repayment status, calculate the ALL; ALL $60= ($1200 * Loss rate 10.2% * LCP 0.5). Construction Yr.2009 Yr.2010 Yr.2011 Average balances Average loss rate Bucket 3 Recorded ALL $100 $110 $105 $108 Recorded $1000 $1100 $1050 $1050 10.2%= $108/$1050 exposureThe estimated loss rate almost has the same rate of the historical recorded allowances, but with the LCP, the ALL shouldbe different, compared to the old CBE GAAP.Page 14 Market practice series “Credit losses modeling”
  15. 15. CBE new GAAP Credit losses assessment Corporate collective ALL should be calculated based on the default rates; that is the historical average recorded ALL „as per the balance sheet‟ divided by outstanding loans per loan segment. Reference: Default rates: CBE new GAAP, page 242, “Collective ALL basis of calculation for the corporate loans”. LCP: CBE new GAAP, page 257-258, “Collective ALL basis of calculation”. e.g. a corporate direct loan portfolio at a total value of $200 & other revolving loans credit commitments of $20, as of 12/31/2012, assigned a risk rating of B „R.R.6‟, calculate the ALL; ALL= ($200+$20) * Loss rate 10.5%= 11. Construction Yr.2009 Yr.2010 Yr.2011 Average balances Average loss rate Risk rating 6 Recorded ALL $100 $110 $105 $108 Recorded $1000 $1100 $1050 $1050 10.2%= $108/$1050 exposureThe estimated loss rate almost has the same rate of the historical recorded allowances.Page 15 Market practice series “Credit losses modeling”
  16. 16. CBE new GAAP Credit losses assessment As per page 257 in the new CBE GAAP, the CBE opened the door for other credit risk modeling approaches that may rely on algebraic or statistical equations.► However, the CBE made it conditioned to the below restrictions to be considered with any adopted approach:1. The time value of money,2. The credit lines different maturities,3. The adopted approach should derive the incurred losses as per IAS 39.► The wide difference in the market practices & confusion about the credit risk measurement in line with the CBE new GAAP was due to the below:1. The LCP was not clarified within the CBE guidelines,2. The CBE opened the door for other approaches to be used which might deploy statistical models, however most of the used statistical models primarily derive the “expected value”, rather than the “incurred value” of losses,3. The default rates as set by the CBE (average ALL/ Loans) will result in the same loss rates as per the CBE old GAAP,4. Finally, It wasn‟t crystal clear whether the credit commitments over the revolving loans should be subject to assessment of impairment on gross basis.Page 16 Market practice series “Credit losses modeling”
  17. 17. CBE new GAAP Credit losses assessment► The wide difference in the market practices and confusion about the credit risk measurement in line with the CBE new GAAP is due to the incomplete guidance and unclear instructions about the rationale and basis of calculation of the IRB-based components, so we maintained to develop our rationale in this presentation from two main references in addition to the CBE new GAAP guidelines „originally adoption to IFRS‟; Basel II, US Federal reserve interpretations of the credit losses measurement; in a way to derive the incurred losses rather than deriving the expected losses as per CBE new GAAP CBE new GAAP confused models Incurred loss „IFRS‟: IL= EAD*PD*LGD Old GAAP default rates: Retail: IL= (EAD * LCP * Average historical allowance rate) Corporate: IL= (EAD * Average historical allowance rate) Statistical models to derive the probability of default „EL‟: EL= E(EAD)*E(PD)*E(LGD)Page 17 Market practice series “Credit losses modeling”
  18. 18. Expected vs. Incurred credit losses
  19. 19. Expected vs. Incurred credit lossesIncurred losses IFRS:► Further, the IASB explains that the accounting model adopted is based on incurred losses (rather than, say, expected losses and certainly not on future losses). It believes that such a model, which does not take account of future events or transactions, is more consistent with an amortized cost basis of accounting► The Board reasoned that it was inconsistent with an amortized cost model to recognize impairment on the basis of expected future transactions and events. The Board also decided that guidance should be provided about what incurred means when assessing whether impairment exists in a group of financial assetsIFRS, IAS 39 Impairment BC 110.Expected Losses Basel II:► That‟s the future credit losses expected to be incurred in case of default of the financial security‟s issuer, including and not limited to any contingent obligations, accrued fees, accrued interest, and any potential payments to collect the default loanPage 19 Market practice series “Credit losses modeling”
  20. 20. Credit risk measurement1. Credit exposure segmentation
  21. 21. Credit risk measurement 1. Credit exposure segmentationLoan portfolio segmentation:► Within the retail asset class category, banks are required to identify separately three sub-classes of exposures: (a) exposures secured by residential properties, (b) qualifying revolving retail exposures, and (c) all other retail exposures► Segmentation at a sub-portfolio level should be consistent with the bank‟s segmentation of its retail activities generally. Segmentation at the national or country level (or below) should be the general rule► Data on loss rates for the sub-portfolio should be retained in order to allow analysis of the volatility of loss ratesSource: Basel II e.g., The secured credit cards‟ holders tend to maintain more frequent pastdues than the unsecured c.c. holders, by mixing the two portfolios in the calculation of the PD & LGD, we maintain to keep an over-estimated credit losses „inherent from secured cards probabilities‟.Page 21 Market practice series “Credit losses modeling”
  22. 22. Credit risk measurement 1. Credit exposure segmentation► The goal of segmentation is to provide meaningful differentiation of the risk, with each pool composed of exposure with homogeneous credit risk, accordingly banks should consider the risk drivers, while developing the risk segmentation► Segmentation should use relevant borrower risk characteristics that reliably differentiate a segment‟s risk from the other segments and perform consistently over time; such as (credit score, loan delinquency, debt-to- income ratio, product, loan to value ratio, origination age, geography, exposure amount, origination channel, ..etc.)► A validation process should be in use to validate the manner upon which the bank differentiated its loan portfolio into segmentsSource: US Federal reserve system, Federal register Vol.69, 2004 notice.► For instance the project finance loans tend to bear higher risk than the ordinary term loans, on the other hand the granted loans to Iraqi region tend to bear higher risk than the other loans granted to other regions, also the loans granted to the tourism sector tend to bear different level of risk, compared to other loans granted to the food and beverage sectorPage 22 Market practice series “Credit losses modeling”
  23. 23. Credit risk measurement 1. Credit exposure segmentation► Credit process and potential what can go wrong: Credit assessment Credit monitoring Provisioning and approval Credit policy Underwriting Settlement monitoring procedures. Credit losses assessment e.g., Increasing debt burden. e.g., weak underwriting policies. Branch compliance with the Portfolio analysis and obligor Corrective action credit policy. follow-up e.g., non-compliance with the e.g., poor industry . e.g., trend of losses might require credit policy, or fraudulent credit reshaping the credit policy, underwriting. approval process, and/or the monitoring phase.Page 23 Market practice series “Credit losses modeling”
  24. 24. Credit risk measurement 1. Credit exposure segmentationRetail portfolio Branch Geography Product SectorCurrent - - - -Bucket 1 - - - -Bucket 2 - - New product 19% Tourism 10%, Aviation 5%Bucket 3 - Aswan 15% - Tourism 7%Bucket 4 Batal 4% Giza 4%, Cairo 8% Club 7%, Car 15% Tourism 3%NPL „100% EL‟ Wadi Degla Br. 27%, Batal 8% Giza 3% Car loans 9% Tourism .5%Credit risk concentration is calculated as below:► Branch concentration: 2 branches had B4 of 10% in relation to the total branches portfolio. Product concentration: (New product portfolio/Total loan portfolio) or (Bucket balance/Total New product)Basis of segmentation (credit risk pooling):► Basis comes from the loan portfolio concentration, for instance a retail portfolio of $10,000, might „ve two products, one accounting for $9,500 and a new product with weak underwriting that accounts for $500, thus the pastdues concentration should be based on two separate product portfolios rather than to the total retail portfolioPage 24 Market practice series “Credit losses modeling”
  25. 25. Credit risk measurement 1. Credit exposure segmentation► Ultimate retail segmentation could look as below: Wadi Branch Mohandseen Other branches 27% 12% Tourism Aviation Other Tourism Aviation Other Tourism Aviation Other 20.5% 5% Sectors 20.5% 5% Sectors 20.5% 5% Sectors New product New product New product New product New product New product New product New product New product 19% 19% 19% 19% 19% 19% 19% 19% 19% Car loans Car loans Car loans Car loans Car loans Car loans Car loans Car loans Car loans 24% 24% 24% 24% 24% 24% 24% 24% 24% Other loans Other loans Other loans Other loans Other loans Other loans Other loans Other loans Other loansSegmentation analysis:► In order to easily identify the loss making sub-portfolio for segmentation purposes, an analysis of the volatility of the incurred losses can be made through calculating the standard deviation SD of the historical loss rates of the sub-portfolio under analysis divided by the aggregate segment loss rate to figure out the segments with high (SD/Avg. loss rate)Page 25 Market practice series “Credit losses modeling”
  26. 26. Credit risk measurement 1. Credit exposure segmentation► Ultimate corporate loan segmentation could look as below: Tourism 10% Construction 13% Other sectors Project Term loans Revolving Project Term loans Revolving Project Term loans Revolving finance 5% loans 20% finance 2% loans 5% finance 1% 50% loans 3% 19% 10%Page 26 Market practice series “Credit losses modeling”
  27. 27. Credit risk measurement2. Exposure At Default “EAD”
  28. 28. Credit risk measurement2. Exposure At Default “EAD”Exposure At Default:► For both the direct and indirect credit exposure; All exposures are measured gross of specific provisions or partial write-offs that might be subject to credit loss.► For revolving exposures such as credit cards and overdrafts, each loan EAD should include both; the outstanding exposure plus estimated net additions to balances for loans defaulting over the following period.► The net additions preceding a credit event are supposed to be a rate equal to CCF, extended to the difference between the authorized credit limit & the outstanding exposure.► Changes in the underwriting policies, regarding the revolving loans utilization might have a decreasing or increasing significant impact on the CCF, hence the EAD, so banks should consider their policy changes, when developing its CCF estimates.► EAD= Outstanding (Principal + Accrued interest +or- deferred fees, premium, discounts – collateral value) + (CCF * Unused credit commitment).Page 28 Market practice series “Credit losses modeling”
  29. 29. Credit risk measurement2. Exposure At Default “EAD”Credit Conversion Factor “CCF”:► The CCF should differ according to whether the exposure is being committed or uncommitted.► A credit line is considered uncommitted if it may be unconditionally cancelled without prior notice, which in turn should bear less CCF rates.► CCF: Credit conversion factor, alternatively known as Loan Equivalent Exposure „LEQ‟► For accounting purposes; the estimated allowances for the credit commitment should be separately disclosed as credit commitments‟ provisions rather than as being part of the allowance for loan losses.► The collateral value should be once considered, whether as part of the EAD or as part of the LGD calculation.Page 29 Market practice series “Credit losses modeling”
  30. 30. Credit risk measurement 2. Exposure At Default “EAD”Credit Conversion Factor “CCF” (cont‟d):► Two main methods:1. The Cohort method: under which the CCF is the average % of the additional drawings for a defaulted credit exposure at a time period, compared to the original exposure amount at time of default, in one exposure segment.2. The fixed-horizon method: under which the CCF is the average % of the additional drawings for a defaulted credit exposure at a time period, compared to the exposure amount at certain date, regardless of the default date, in one exposure segment.► Regardless of the adopted method, the CCF can‟t be negative, thus only the additional drawings in one exposure or loan segment should be assumed in the CCF calculation.► We have adopted the cohort method in this guidance.Page 30 Market practice series “Credit losses modeling”
  31. 31. Credit risk measurement 2. Exposure At Default “EAD”Illustrative case for the EAD, using cohort method:► ABC construction Co. has been granted the below credit lines ► Overdraft $1000, Term loan $200 ► Risk rating 3, CCF 15%► At end of the FY2011, the outstanding exposure withdrawn principal + accrued interest +/- deferred charges has been as below ► Direct exposure: Overdraft $950, Term loan $198 ► Indirect exposure: OD credit commitment $50 ($1000 - $950)► EAD= $198 + $950*15%► CCF: calculated based on historical conversion rates for similar (risk rated and industry) obligors, for instance; it has been noted that the average downgraded obligors from RR.2 to RR.3 had the below utilization history: Available limit Period 1 Period 2 CCF% For the downgraded portfolio RR.2 $100 RR.3 „downgraded, originally from RR.2‟ $75 15%= ($100-$75)/$100Note: the downgraded exposure should reflect the increased exposure alone, rather than considering the a whole balance that reflects the offsetof both the paid-off exposure „LGD‟ and the extra utilization „CCF‟.Page 31 Market practice series “Credit losses modeling”
  32. 32. Credit risk measurement3. Measurement methods (Historical charge-off method)
  33. 33. Credit risk measurement 3. Measurement methods (Historical charge-off method)The graph below represents a loan portfolio over a time length of four years, showing the change in therisk ratings.► A historical charge-off analysis intends to derive the historical charge-off rate per loan segment, extended to the period it takes to be a confirmed loss.► For instance the project finance loan portfolio looks to bear historical CCC-rated loans of within an average of 40% to 60% to the total portfolio, compared to the other commercial loans, which looks to bear around 15% historical loss rate.► A loss confirmation period would capture how long it takes a loan to be a confirmed loss, thus if the other commercial portfolio borrowers take an average of two years to be a confirmed loss, then the loss rate should be adjusted from 15% to 30%; meaning there are some other 15% incurred losses, but still passive to the creditor. Loan portfolio Other Commercial Project finance 100% 100%100% 80% 80%80% 60% 60% AAA+BBB2 60% 40% AAA+BBB 40% 20% 20% CCC 40% CCC AAA+BBB 0% 0% 20% 0% CCCPage 33 Market practice series “Credit losses modeling”
  34. 34. Credit risk measurement 3. Measurement methods (Historical charge-off method) Historical loss rateIllustrative case for the historical loss rates:► ALL= (EAD*Historical Loss Rate*Loss Confirmation Period)► Example of a loan portfolio:New product Period 1 Period 2 Average„assumed risk pooling per product‟EAD $1200 $2300 $2300 „Per.2‟Current $1000 $2000 $1500Net charge-offs $100 $50 $75NPL „100% EL‟ $300 $400 $350Historical loss rate 40% 23% 28% = ($100+$300)/$1000 = ($50+$400)/$2000Environ‟l adj.* 4%Total Allowance for Loan Losses „ALL‟ $1,339 = (28%* 1.04) *$2300* 2Yr.For simplicity, the LCP is assumed to be 2 years „credit line tenor.Page 34 Market practice series “Credit losses modeling”
  35. 35. Credit risk measurement 3. Measurement methods (Historical charge-off method) Loss Confirmation Period “LCP”► Loss Confirmation Period “LCP”: that‟s through examining the past defaults/charge-offs, the creditor determines that on average the borrower takes certain time period before it defaults, for instance a retail loan would take 6 months to default moving from current portfolio to NPL „100% EL‟ , however a corporate loan would take 2.5 year to default on average, since the borrower rescheduling or pastdues tend to start after a weaken financial strength has taken place.Source: US GAAP “interpretation of the incurred losses Yr.2003”.► The CBE temporarily set the Loss Confirmation Period to 1 for the 1st year of adoption of the new CBE GAAP, however banks are required to develop their own.► Another definition can be found in Basel II, known as the EM, also known as Macaulay duration.Page 35 Market practice series “Credit losses modeling”
  36. 36. Credit risk measurement3. Measurement methods (Historical charge-off method) Loss Confirmation Period “LCP”► The Effective Maturity “EM”: that‟s the maximum remaining time (in years) that the borrower is permitted to take to fully discharge its contractual obligation (principal, interest, and fees) under the terms of loan agreement► One year floor doesn‟t apply to short-term exposures, this floor is only available for short-term exposures with an original maturity of below one year,► Effective Maturity (M) = Σ t* CF/ΣCF. „Basel II‟Page 36 Market practice series “Credit losses modeling”
  37. 37. Credit risk measurement3. Measurement methods (Historical charge-off method) Loss Confirmation Period “LCP”Illustrative case for the Loss Confirmation Period:► Retail loan portfolio, based on the bank policy, we have the below: ► Credit event is to have a loan with pastdues for > one day, ► NPL is the loans with pastdues of > 60 days ► Buckets are Current „no pastdues‟, B1 „<30 days pastdue‟, B2 „<60 days pastdue‟, NPL „>60 days‟ ► Borrowers have been tracked through a history of one year, identifying the 1st time of the loss trigger „credit event‟NPL Historical data NPL EAD Period Weight Yr*W„100%EL‟ Yr W Q4.2010 Q3.2010 Q2.2010 Q1.2010 Q1.2011Cust#1 NPL B2 B1 Current $100 0.6Yr= 51%= 3* 0.3Yr (30+2*90) $100/$600Cust#2 B2 B1 B2 B1 $200 1.1Yr= 99%= 3* 1 Yr (30+4*90) $200/$600Cust#3 B2 B1 Current B2, B1 in Q4.09 $300 1.6Yr= 150%= 3* 2.3Yr (30+6*90) $300/$600Total $600 LCP= Max( 1Yr, Av.Period ) 1.2YrPage 37 Market practice series “Credit losses modeling”
  38. 38. Credit risk measurement3. Measurement methods (Historical charge-off method) Loss Confirmation Period “LCP”Illustrative case for the EM or Macaulay duration:► A loan of $300, with yearly repayment plan of $10 over 3 years, in addition to one last payment of $300 at the maturity time, find the LCP at time of inception: ► Macaulay duration can be simply the remaining life of the security= 3Yrs, ► or 2.9Yrs= [(1*$10/$330)+ (2*$10/$330)+ (3*$310/$330)] Time 0 Yr.1 Yr.2 Yr.3 -$300 $10 $10 $10 + $300 Cash outflow Cash inflow Cash inflow Cash inflow EM= 2.91 EM= 1.91 EM= 0.94 =(1*10/330)+ =(1*10/330)+ =(1*310/330) (2*10/330)+ (2*310/330) (3*310/330)Page 38 Market practice series “Credit losses modeling”
  39. 39. Credit risk measurement3. Measurement methods (Migration analysis- simplex method)
  40. 40. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method)The graph below represents a loan portfolio over a time length of four years, showing thechange in the risk ratings.► A migration analysis intends to derive a loss rate, which is the probability that a AAA- rated loan would become CCC-rated, less the probability that a due loan could be recovered over a certain period of time.► A credit event for a loan would be the loss trigger that kicks a loan from one risk rating to another.► The higher the degree of segmentation, the higher the accuracy of deriving the risk components. Loan portfolio Other Commercial Project finance100% 100% 100%80% 80% 80% 60% CCC 60% CCC 60% 40% 40% 20% BBB 20% BBB 40% 0% 0% CCC AAA AAA 20% BBB 0% AAAPage 40 Market practice series “Credit losses modeling”
  41. 41. Credit risk measurement3. Measurement methods (Migration analysis- simplex method)The risk components can be calculated per transaction or at the credit exposure segmentlevel.For those who prefer to do their calculations on borrower/ transactional level, then aconversion „if needed‟ to the segment level can be done as illustrated below:Credit exposure segment: Project finance loans for the telecommunication sectorCustomer Historical data Transaction Risk components Weighted Average rate for the selected weight (PD, LGD, LCP) exposure segment Yr.2010 Yr.2011 Yr.2012 W i1 W i2 W i3 Yr.2010 Yr.2011 Yr.2012Cust#1 $1000 $800 $600 67% 50% 35% 3 1 2Cust#2 $200 $300 $700 13% 19% 41% 20 15 10Cust#3 $300 $500 $400 20% 31% 24% 30 5 15Total $1500 $1600 $1700 100% 100%100% 10.6* 4.9 8.4 7.97= Av.(10.6, 4.9, 8.4)*10.6: is the risk component, weighted by the transaction size, which is numerically derived from the [(3*67%)+ (20*13%)+ (30*20%)]= 10.6,The input digits could be percentage or numbers, could be PD, LGD, or LCP.Page 41 Market practice series “Credit losses modeling”
  42. 42. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Credit and recovery event
  43. 43. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Credit and recovery eventCredit (default) event:► That‟s the loss trigger that indicates that a loss has been incurred, which may lead to eventual loss „or default‟, for instance it could be the event of a transaction or credit exposure to be downgraded.► The definition of the credit event significantly impacts the calculation of the risk components, thus the loan portfolio should be cross segmented based on the credit event, for instance the retail loans would be segmented based on the repayment status, and the corporate portfolio would be segmented based on the risk ratings, thus deriving representative loss rates “PD*LGD”, reflecting the sector, risk rating/ repayment status, product risk,…etc.► It should be noted that the NPL with 100% of expected losses should be defined in light of the regulatory requirements. Retail based on Current Bucket 1 Bucket 2 Bucket 3 NPL „100% EL‟ repayment status 30 „31-90‟ „91-180‟ „181-270‟ „>270‟ Corporate based Risk rating Risk rating Risk rating NPL on risk rating 1-3 4-5 6-7 8-10Page 43 Market practice series “Credit losses modeling”
  44. 44. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Credit and recovery eventIllustrative case for the calculation of the allowance for loan losses:► After defining the credit event and the portfolio segmentation has been made, the Allowance for Loan Losses can be calculated for each credit exposure segment as illustrated below: Current Bucket 1 Bucket 2 Bucket 3 Bucket 4 NPL New product $465 EAD $100 EAD $120 EAD $110 EAD $70 EAD $65 „100% EL‟ EAD PD1 0.6% PD2 46% PD3 45% PD4 70% PD5 69% LGD1 100% LGD2 63% LGD3 73.4% LGD4 73.5% LGD5 79% New product EAD*PD*LGD EAD*PD*LGD EAD*PD*LGD EAD*PD*LGD EAD*PD*LGD Allowance $143 $100*0.6%*100% $120*46%*63% $110*45%*73.4% $70*70%*73.5% $65*69%*79%Page 44 Market practice series “Credit losses modeling”
  45. 45. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method) Credit and recovery eventCredit (default) event for a corporate obligor:► A default for a corporate obligor is subject to the whole outstanding lines for the borrower rather than a particular credit line as for the retail obligors.► A default is considered to have occurred with regard to a particular obligor when either one or more of the following events have taken place:1. The bank considers that the obligor is unlikely to pay its credit obligations to the banking group in full, without recourse by the bank to actions such as realizing security (if held),2. The obligor is past due for 3 installments or more on a material credit obligation to the banking group. Overdrafts will be considered as being past due once the customer has breached an advised limit or been advised of a limit smaller than current outstanding,3. The bank makes a charge-off or account-specific provision resulting from a significant perceived decline in credit quality subsequent to the bank taking on the exposure,Page 45 Market practice series “Credit losses modeling”
  46. 46. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method) Credit and recovery eventCredit (default) event for a corporate obligor (cont‟d):4. The bank consents to a distressed restructuring of the credit obligation where this is likely to result in a diminished financial obligation caused by the material forgiveness, or postponement, of principal, interest or (where relevant) fees,5. The bank has filed for the obligor‟s bankruptcy or a similar order in respect of the obligor‟s credit obligation to the banking group, or the obligor has sought or has been placed in bankruptcy or similar protection where this would avoid or delay repayment of the credit obligation to the banking group,6. The bank sells the credit obligation at a material credit-related economic loss,7. Whether any of the above has resulted in a downgrade, a downgrade by itself is considered as a default event.Page 46 Market practice series “Credit losses modeling”
  47. 47. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method) Credit and recovery eventCredit (default) event for a retail obligor:► The retail credit event is different from the corporate in a way that the retail is applicable to a particular loan rather than the underlying outstanding exposure of the borrower.► A particular retail exposure is considered defaulted if one of the below events have taken place:1. A partial or full charge-off has been taken place against its exposure,2. A retail obligor has filled for bankruptcy,3. A retail borrower has missed one or more payments of the due principal, interest , or fees.Page 47 Market practice series “Credit losses modeling”
  48. 48. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Credit and recovery event► A credit risk measurement policy should numerically define the credit and the recovery event. Credit event:► For instance; a loan should be considered in default, regardless to the risk rating, thus a 3 installment due loan, will be considered as an observation of default, impacting the PD & LGD calculations.► A sold-off credit exposure would be; that any sold-off loan with a market yield at time of disposal greater than its original EIR due to a deteriorated credit quality, then it should be considered as a default, impacting the PD & LGD calculations. For instance a more than 30% increase in the sale yield should be assessed for impairment whether it‟s been due to a credit deterioration. Recovery event:► For instance; a credit exposure would be considered a recovery if at least 90% of its defaulted due fees, interest, and principal have been settled.► The recovery event is explained through the LGD section.Page 48 Market practice series “Credit losses modeling”
  49. 49. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”
  50. 50. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”Probability of Default PD:► For corporate and bank exposures, the PD is the greater of the one-year PD associated with the internal borrower grade to which that exposure is assigned. For sovereign exposures, the PD is the one-year PD associated with the internal borrower grade to which that exposure is assigned. The PD of borrowers assigned to a default grade(s), consistent with the reference definition of default, is 100%.Source: Basel II► The one-year default rate (or default frequency) is the number of accounts that default at any time within the period divided by the number of accounts open at the beginning of the year. A validation mechanism should be deployed in case of using the $$ value in estimating the PD rather than the number of accounts.Source: US Federal reserve system, Federal register Vol.69, 2004 notice► Segmenting the loan portfolio on (credit line size) basis to derive the PD, using the $$ value approach should be an easy-smart alternativePage 50 Market practice series “Credit losses modeling”
  51. 51. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”Probability of Default PD:► PD should capture all the credit event observations for a credit exposure segment over certain time-horizon.► For instance; if the a credit event such as rescheduling, past-due default is not being reflecting on the risk rating, then the PD calculation should consider all such credit events as observations, as explained below: Risk rating Period1 Period2 Downgrades Downgrades Rescheduled Defaulted on PD to BBB to DDD “Not TDR” 3 installment AAA $100 $5 $10 $15 $5 35%= ($5+$10+$15+$5)/ $100Page 51 Market practice series “Credit losses modeling”
  52. 52. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”► Two main methods used to calculate the PD: 1. Unit/ account based PD “for retail”, 2. $$ value based PD “for corporate & retail”.► The unit based PD supporters seem to view the PD from the number of occurrences rather than from the exposure defaulted.► For instance; an $800 retail loan portfolio, composed of 100 accounts, one main account with a total value of $500, and the others make a total of $300, spread equally. If that one account defaults, then:► $$ value PD 63%= $500/$800,► Unit PD 1%= 1/100.► Alternative approach would be based on segmenting the loan portfolio over two (one account making $500), and (99 accounts making $300), so the difference between the unit & $$-value PD should reasonably come to a small margin.Page 52 Market practice series “Credit losses modeling”
  53. 53. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”► If the bank adopts $$ value PD, then it should consider the gross loan value rather than net of the collateral value, thus deriving the default probability of the risk ratings, industries in a portfolio that might have been incurred but not observed in the fully or substantially covered credit lines e.g., Listed below are the credit limits that were granted for the aviation industry: ► ABC Air Co., $1000, fully cash/bank guarantee covered, market share 70%, ► XYZ, $1000, 40% covered, market share 20%, ► ABC Co., $1000, 0% covered, market share 10%, ► Assuming same risk ratings at time of initiation, however at year end, ABC Air Co. alone has been downgraded from RR.2 to RR.6; If we calculate the PD, based on gross loan balances, then the derived PD will reflect the whole deterioration in the credit risk in the aviation industry, as the downgraded credit lines will account for $1000, however if we calculate the PD based on credit exposures net of the collateral value, then the derived PD won‟t reflect the deterioration in the credit exposure with the aviation sector, as the downgraded credit lines will account for $0, ► The later mentioned PD is understated in light of the fact that the downgrading credit exposure is being „hidden‟ by the cash cover, however in fact it represents a credit exposure to 70% of the aviation sector.Page 53 Market practice series “Credit losses modeling”
  54. 54. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”Illustrative case 1 for the PD ($$-value):► Retail portfolio New product Period 1 Period 2 PD1 PD2 Average PD Cumulative „Outstanding dues‟ „given‟ calculated Av.(PD1, PD2) PDCurrent $1000 $2000 40% 60%=$600/$1000 50%=Av.(PD1,PD2) 9%Bucket 1 $800 $600 30% 88%= $700/$800 59%=Av.(PD1,PD2) 18%Bucket 2 $600 $700 50% 83%= $500/$600 67%=Av.(PD1,PD2) 31%Bucket 3 $400 $500 60% 75%= $300/$400 68%=Av.(PD1,PD2) 46%Bucket 4 $300 $300 70% 67%= $200/$300 68%=Av.(PD1,PD2) 68%NPL „100% EL‟ $100 $200The Allowance for Loan Losses should then be calculated as EAD*(PD „Col.#7‟+ Environ‟l adj.)*LGD.* Environ‟l adj.: Environmental adjustment, standing for the incurred credit losses but notyet observed in a form of default in the credit portfolio, derived from the change inaverage historical PDs compared to the PDs at time of the crises.Note: the environmental adjustment can be done to the total loss rate (PD*LGD) insteadof segregating it to the PD and to the LGD using the same rationale mentioned above.Page 54 Market practice series “Credit losses modeling”
  55. 55. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”Illustrative case 2 for the PD ($$-value):► Retail portfolioHistorical loss and delinquency data (simplified example) March April May JuneCurrent $2,500 $2,640 $2,600 $2,67530 DPD $90 $100 $120 $14060 DPD $42 $45 $47 $4990 DPD $37 $36 $37 $39Charge-off $29 $31 $32 $33Roll rates April May June $140/$2600 3 mo.avg.Cur-30DPO 4.00% 4.55% 5.38% 4.64% (4.00% + 4.55% + 5.38%30DPO – 60DPO 50.00% 47.00% 40.83% 45.94%60DPO – 90DPO 85.71% 82.22% 82.98% 83.64%90DPO – Charge-off 83.78% 88.89% 89.19% 87.29%Estimated credit losses: July August Sept.Current $2,641 $2,605 $2,558 $2605*30 DPD $124 $123 $12160 DPD $64 $57 $5690 DPD $41 $54 $48 3 month loss $34 + $36 + $47Charge-off $34 $36 $47 $117Page 55 Market practice series “Credit losses modeling”
  56. 56. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Probability of Default “PD”Illustrative case for the PD ($$-value):► Corporate portfolio Period 1 Period 1 PD1 PD2 Average Cumulative PD Jan.2011 Dec.2011 „given‟ „calculated‟ (PD1,PD2) RR. 1 $7000 $10,000 40% 43%=$3000/$7000 41% 1%= (41%*53%*76%*75%*68%*53%*25%) RR. 2 $9000 $3000 50% 56%=$5000/$9000 53% 3% RR. 3 $4000 $5000 76% 75%=$3000/$4000 76% 5% RR. 4 $5000 $3000 70% 80%=$4000/$5000 75% 7% RR. 5 $3000 $4000 70% 67%=$2000/$3000 68% 9% RR. 6 $2000 $2000 55% 50%=$1000/$2000 53% 13% RR. 7 $1000 $1000 30% 20%=$200/$1000 25% 25% NPL 8:10 $100 $200 NA NAThe Allowance for Loan Losses should then be calculated as EAD*(PD „Col.#6‟+ Environ‟l adj.)*LGD.For simplicity; the identified downgrades are assumed to be just from the previous risk pool.e.g., $3000 identified in RR.2 in period 2 is assumed to be a full downgrade from RR.1 thathad an exposure of $7000 in period 1.Page 56 Market practice series “Credit losses modeling”
  57. 57. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”
  58. 58. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Loss Given Default:► That‟s the share of the defaulted exposure that will never be recovered by the lending bank. The LGD of a transaction is more or less determined by “1 minus recovery rate”, in other words the LGD quantifies the portion of loss the bank will really suffer in case of default. The LGD should be measured as a percentage of the EAD. A bank should provide an estimate of the LGD for each corporate, sovereign and bank exposure.Source: Base II► There are three main approaches as per Basel II, explaining the LGD Standardized, Foundation, and Advanced approach „recommended by CBE‟Page 58 Market practice series “Credit losses modeling”
  59. 59. Credit risk measurement 3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Loss Given Default:► LGD is defined as the segment‟s credit-related economic losses net of discounted recoveries divided by the segment‟s exposure at default, all measured during a period of high credit losses for the particular loan, unlike the PD, reference data sets for LGD contain only defaulted exposure.► The concept of the economic loss is more broader than the accounting measure of loss.► Economic loss incorporates the mark-to-market loss of value of the defaulted loan & collateral plus any direct & indirect costs to collect the loan, net of recoveries, which all should be discounted to the time of default.► The discount rate should be applied to the time period from the date of default to the date of realized loss, or recovery on a pool basis.► The discount rate should reflect the distressed rate of the credit line, in other words the opportunity cost of the time value of money „mark-to-market‟.Source: US Federal reserve system, Federal register Vol.69, 2004 notice”Page 59 Market practice series “Credit losses modeling”
  60. 60. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Loss Given Default general note:► EAD should be the aggregate value of the outstanding loan and any past partial/full charge-off.► In order to better present the incurred losses rather than the expected losses, the exposure should only include the principal plus any accrued interest and fees, same applies for the discount rate; as it „ll better assess the incurred losses through discounting using the original discount rate of the loan rather than being a market rate.► The non-performing retail loans “100% provision” rule can be avoided by supporting how much that portfolio recovers, thus it would be provided for 100% less the percentage of recovery; “100%-LGD%”.Page 60 Market practice series “Credit losses modeling”
  61. 61. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Loss Given Default (Foundation approach):► In the foundation approach, the “basic” loss-given default is fixed at 45% for all senior, unsecured exposures. This value should be raised to 75% for subordinated exposures, but can be adjusted downwards when some recognized collateral is pledged against the loan. However, this reduction can‟t be based on a bank‟s internal models or past experience. Instead, a set of rules has been introduced that quantify the effect of financial and non- financial collaterals.► An adjusted formula for LGD* can be calculated as below: LGD*= (45% or 75%).Max[0,1+HE-C/E(1-Hc-Hfx)] C: Collateral value E: Original exposure value HE: Haircut rate to be added to the value of the exposure Hc: Haircut rate the collateral, reflecting the risk of the collateral market value Hfx: FOREX haircut, if a currency mismatch exists between the exposure and the collateral Note: Haircut rates are the higher of regulatory or the internally developed ratesPage 61 Market practice series “Credit losses modeling”
  62. 62. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Loss Given Default (Foundation approach) :► For banks applying its IRB approach “Internal Ratings Based”, haircuts are replaced by a system of minimum and maximum haircuts, as below: LGD*= (45% or 75%) less: Max[0,{(Min(C/E, Tmax) – Tmin}/{Tmax-Tmin}].(45%-LGDmin) Tmax: Maximum threshold for the C/E ratio, based on the collateral type Tmin: Minimum threshold for the C/E ratio, based on the collateral type LGDmin: Minimum ratio when C/E >= TmaxPage 62 Market practice series “Credit losses modeling”
  63. 63. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Loss Given Default (Advanced IRB approach):► Adopting this approach will permit banks to use their own estimates of LGDs for the corporate portfolio, however for the retail portfolio there, only the advanced approach should be adopted► Moreover, the Basel Committee states that exposure risks on retail loans with uncertain future drawdown (such as credit cards) may be incorporated into LGD estimates, accounting for the expectation of additional drawings prior to default► In other words, when a bank does not reflect risk on undrawn lines in its EAD estimates, it should reflect this in its LGD estimates. For example, if the bank estimates that EAD on a retail pool will be 20% higher than current usage, LGD can be increased accordingly (e.g., from 50% to 60%) to account for exposure risks without having to establish a formal system of CCF on undrawn revolving credit linesPage 63 Market practice series “Credit losses modeling”
  64. 64. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Loss Given Default (Advanced IRB approach) :► The basis of calculation is based on the ratio between the present value, at the time of default, of all payments made on a defaulted debt instrument, and the face value (plus any accrued interest) of this instrument, which can be expressed as follows: LGD = 1-Recovery rate Recovery rate= [ {(FR-AC)/EAD}/(1+r)t ] Alternatively, the LGD= Gross defaulted exposure/EAD In order to derive the LGD, an observation should be witnessed, which is based on the Bank recovery policy, for instance the recovery policy for a two-risk rated loan is at least 85% recovery rate, thus LGD should be calculated as being the average rate for the observations of 85% or more as a recovery rate for a B-risk rated loan. FR: Face value of the Recoveries AC: Amount of Costs associated with the recovery process. r: The original effective interest rate of the credit line. t: Work-out period or the recovery period, defined as the period from the date of default to a resolution date. A resolution date should be defined whether it‟s the date of 100% settlement or 95% settlement of the default exposure or otherwise based on the institutions policy.Page 64 Market practice series “Credit losses modeling”
  65. 65. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Illustrative case for the recovery rates for a retail loan portfolio: Assuming:► The bank policy defines the credit event loan as being the downgrade from bucket to another, with 30 days dues time length for the bucket, therefore the recovery event is the reverse, which is a retail loan to get upgraded from a bucket to another. Time-horizon is quarterly data, and the presented loan exposure is for a 2-Yr, quarterly installment loan with a fixed interest rate of 10%. Initially the defaulted exposure was for $20 „value of 1st due installment‟ out of his original loan amount of $143.3, later he paid a total of $22.61; Pastdues Period 1 Period 2 Period 3 Recovery rate LGD Current - - $92.9 NA Bucket 1 $147 (including $114.7 (including - 95%= [PV(r=10%/4, t=3 5%= 1-95% $20 pastdues) $2 pastdues) periods to recover,, FR=$22.61)/ EAD=$20+$2]In case that the settled amount is not tracked on system, and can‟t be 93%= [PV(r=10%/4, t=3 7%= 1-93%obtained the recovery rate would be based on the amount originally periods to recover,,defaulted at. FR=$20+$2)/ EAD=$20+$2]Page 65 Market practice series “Credit losses modeling”
  66. 66. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Detailed basis of calculation: NPER EIR 4 10% Period PMT Interest $143.4 Past-dues Collected Recovery Rate LGD (Defaulted exposure) 1 -20 $3.59 $146.99 20 0 2 -20 $3.67 $114.66 2 18 $0.00 0% 3 -20 $2.87 $92.92 0 4.61 $21.00 95% 5% 4 -20 $2.32 $75.24 5 -20 =PV(10%/NPer, date or period of $1.88 $57.12 recovery, total collected amount) 1- R. rate 6 -20 $1.43 $38.55 5%= 1- 95% 7 -20 $0.96 $19.51 =MIN[100%, PV of the recovered amount 8 -20 $0.49 $0.00 $21/ Defaulted exposure ($20+$2)]Page 66 Market practice series “Credit losses modeling”
  67. 67. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Illustrative case for the recovery rates for a revolving loan portfolio: Assuming:► Same as per the last illustrative case, but with a credit card exposure of $500 instead; Pastdues Period 1 Period 2 Period 3 Recovery rate LGD Current - - NA Bucket 1 $500 - - NA Bucket 3 - $200 $10 93%= [PV(10%, 9 months out of 1Yr, FR= 7% =1-93% $500-$10) / EAD= Max(500, 200)]Page 67 Market practice series “Credit losses modeling”
  68. 68. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Illustrative case for the recovery rates for a corporate loan portfolio: Assuming:► ABC has been downgraded in year 2, without being past-due, however his industry perspective has been a bit speculative, meanwhile XYZ has been unable to repay the last due 3 installments, finally JOE has been struggling to pay off his dues with other banks, but before he comes due on installment with our bank, he agreed to reschedule his debts,► In later periods however, XYZ has been able to pay off the due installments over one year; thus the recovery rate for risk rating 1 is: Risk Yr 1 Yr 2 Past-dues Resched Recovery rate LGD rating „> 3 uling installments‟ 1 ABC $500 - - - XYZ 91% =[PV(10%/4 periods, 9%=1-91% XYZ $800 XYZ $800 XYZ $100 - Recovery period 4 quarters,, FR JOE $300 JOE $300 - JOE $300 $100)/ EAD $100] 2 - ABC - - NA $500Page 68 Market practice series “Credit losses modeling”
  69. 69. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Types of LGD:► Ordinary LGD: LGD= 1-Recovery rate, Recovery rate= [ {(FR-AC)/EAD}/(1+r)t ]► Collateral weighted LGD: LGD= 1-Recovery rate, Recovery rate= [ {(FR-AC)/EAD*}/(1+r)t ] EAD*= EAD x [C/E(1-Hc-Hfx)]► Downturn LGD: LGD= Average LGDs at time of a past crisis or to be adjusted by the average change in PDs from the ordinary time to the time of the crisis► Default weighted LGD: LGD*= LGD x PDwiPage 69 Market practice series “Credit losses modeling”
  70. 70. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Loss Given Default “LGD”Illustrative case of the Default weighted LGD:LGD*= LGD+(PDwi x LGD) PD PD wi LGD Wi= (PD wi x LGD) LGD*= (LGD +Wi) Bucket 1 30% 20% = 30%/130% 10% 2% 12% = 10%+2% Bucket 2 40% 30% = 40%/130% 10% 3% 13% = 10%+3% Bucket 3 60% 50% = 60%/130% 10% 5% 15% = 10%+5% Total 130%Page 70 Market practice series “Credit losses modeling”
  71. 71. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Trade finance
  72. 72. Credit risk measurement3. Measurement methods (Migration analysis- simplex method) Trade financeBasis of deriving the risk components: Letter of Credit Letter of Guarantee Other products Default definition: Based Default definition: Based Default definition: Should on the frequency of on frequency of be based on the liquidation* and/or liquidation and/or frequency of liquidation stagnancy** stagnancy. or other technical (Import LC, confirmed (Performance LG, Bid assessment that asserts export LC, ..etc) LG) when a contract or Same as direct exposure product has been (Debt LG) defaulted. * Liquidation frequency: Is stated to be the number of times the credit lines per certain risk rating are being converted into direct loans. e.g., 3 credit lines (LC, LG) at a value of 1mn each were made at the beginning of the year, however only one credit was liquidated at the end of the year due to illiquidity of the obligor at a fee rate/interest rate of 5%, thus the PD for that year is 33%=1/3mn. By end of the following year, the liquidated lines „1mn‟ were fully collected, thus LGD= [1-(1mn/1mn)/(1+5%)^1] * Stagnancy: Is stated to be the case of expiration of the credit line, however still unconverted into a direct loan and couldn‟t be closed due to some technical problems between the line beneficiary and the obligor.Page 72 Market practice series “Credit losses modeling”
  73. 73. Credit risk measurement4. Economic and market assessment
  74. 74. Credit risk measurement 4. Economic and market assessmentThe assessed credit losses should reflect the current economiccircumstances that might bear incurred losses, but not yet observed.► Two main approaches: ► Credit risk stress testing, ► Past observation of historical loss rates at time of high loss severity.► Risk components: ► EAD (CCF should reflect the change in draw down rates at time of high loss rates), ► PD (Should be adjusted to match the slope in the PDs at time of high loss severity), ► LGD (Downturn LGD, default weighted LGD, or an adjusted LGD to reflect the loss rates observed at time of high loss severity).Page 74 Market practice series “Credit losses modeling”
  75. 75. Credit risk measurement5. Model validation and back-testing
  76. 76. Credit risk measurement5. Model validation and back-testingThe validation and back-testing process is mainly consisted of:► Model methodology validation: Intended to assure the logic & soundness of two processes; the exposure risk segmentation, and the quantification of the risk parameters process.► Operational process review: Intended to assure the accuracy of the quantification process of the risk parameters; that they are in line with the designed methodology and that any exceptional or unusual circumstances have been reported to the upper management and properly addressed, The quantification process should entail mapping the calculated risk parameters to the data sets in addition to the mathematical calculations of the risk parameters.► Model back-testing: Intended to reassess the validity of the model through internal or external review, mainly through default rates comparison over time to assess the adequacy of the estimated allowances under the adopted methodology. The validation & back-testing process should be conducted by an independent unit, on periodic basis, on yearly basis at least.Page 76 Market practice series “Credit losses modeling”
  77. 77. Credit risk measurement5. Model validation and back-testingBack-testing example► The Model parameters should be subject to review and approval from the management based on: 1. Internal review (correlation 29.9%!!)10.0% 9.0% 8.0% Example of an internal review; comparing the trend of the 7.0% estimated Allowance for Loan 6.0% Losses ALL to the trend of the 5.0% Non-performing Loans NPL for 4.0% the retail loans. Data extracted from the FS, thus 3.0% NPL should comply with financial 2.0% reporting definition. 1.0% 0.0% Dec.10 Jun.11 Dec.11 R. ALL/ Retail loans R. NPL loans/ Retail loansPage 77 Market practice series “Credit losses modeling”
  78. 78. Credit risk measurement5. Model validation and back-testingBack-testing example► The Model parameters should be subject to review and approval from the management based on: 2. Peer review (correlation 15%!!) 120.0% Example of a peer review; 100.0% comparing the trend of the estimated Allowance for Loan 80.0% Losses ALL to the trend of the Non-performing Loans NPL for 60.0% the retail loans for my bank and a peer bank. *ALL/NPL%: 40.0% Equals the ALL/Retail loans %divided by the NPL/Retail 20.0% loans%. Data extracted from the FS, thus 0.0% NPL should comply with financial Dec.10 Jun.11 Dec.11 reporting definition. ALL/ NPL% "My Bank" ALL/ NPL% "Peer Bank"Page 78 Market practice series “Credit losses modeling”
  79. 79. Credit risk measurement6. Reference data sets
  80. 80. Credit risk measurement6. Reference data sets► Data sets: Data that should be tracked and available for the calculation of the risk components, and for segmentation purposes.► Time horizon: That‟s the period of time by which the credit risk related data sets are plotted in order to derive the risk components for the purpose of calculating the credit losses. For instance the corporate loans data sets are agreed to be on yearly basis, however the retail loans data sets are argued to be on yearly or quarterly basis, based on the bank credit risk policy.► Data coverage period: For the IRB approach, three to five years is being mandated as the minimum period to be covered in order for a bank to use an IRB-based credit risk measurement model.Page 80 Market practice series “Credit losses modeling”
  81. 81. Questionable market practices
  82. 82. Questionable market practices Loan segmentation PD/historical charge-off LGD/LCP EAD rate► Risk ratings alone (misstating ► Average historical recorded ► LGD: Average (recorded ► The direct exposure alone the credit losses due to the credit losses in the P&L to the allowances based on old (understating the credit losses fact that product type, region, credit exposure (ignoring the GAAP/exposure), leading to by an amount = CCF * sector „ve their own loss and fact that the recorded losses (estimating losses in adverse revolving loans commitment) recovery rates) are based on old GAAP, and relationship with the trend of ► All the direct and indirect understating the credit losses NPL)► Certain product level without a exposure (overstating the due to the calculating a proper analysis of high credit ► LGD: Old GAAP loss rates credit losses proportion of the loss instead risk concentrated or (understates the losses, as the of calculating the whole deteriorating segment old GAAP loss rates exposure being under default) (misstating the credit losses; if compensate for the „PD*LGD‟) the most of the portfolio quality ► Rate of migration between the ► LCP: set at the maximum of 1 is clean, then the incurred risk ratings for all the total (understates the losses as it‟s losses over the high loss exposure „direct and indirect‟, usually floored to 1, and making segments will be (very conservative approach termed into years) understated and vise versa) as the indirect exposure losses aren‟t expected to be as large as the direct) ► Assigning the same PD rates originally driven from the direct exposure; to the indirect exposure (overstating the credit losses by as the indirect exposure isn‟t supposed to incur as much as the direct exposure)Page 82 Market practice series “Credit losses modeling”
  83. 83. Credit risk documentation
  84. 84. Credit risk documentation Minimum requirements:1. Credit exposure segmentation 4. Reference data sets A. Definition of a credit exposure A. Time-horizon B. Credit exposure types B. Data coverage period C. Segmentation basis C. Data sets D. Rationale of the segmentation basis D. Mapping the risk components to the data sets2. Measurement method 5. Exceptional & unusual circumstances A. Model scope & purpose A. Basis of treatment B. Adopted measurement method B. Rationale of the treatment i. Historical charge-off 6. Model validation and back-testing ii. Migration analysis A. Review of the model methodology iii. Other structured models B. Review of the operational process C. Basis of calculation of the risk components C. Back-testing D. Definition of the loss trigger & recovery event D. History of the model amendments E. Rationale of selection E. Oversight BOD and management approval3. Economic and market assessment A. Stress testing (objective & scenario basis) B. Other approachPage 84 Market practice series “Credit losses modeling”
  85. 85. FAQ
  86. 86. FAQIRB risk components (EAD, PD, LGD) calculation:1. How should the sold off loan portfolio impact the risk components? The IRB risk components should be adjusted to recognize the risk characteristics of the exposures that removed reference data sets through sales or securitization It becomes substantially important for banks that usually sells off primarily credits that are poorly performingSource: US Federal reserve system, Federal register Vol.69, 2004 notice”2. Should the history data cover a time period of recession? The PD covered period should entail at least one period of recession, furthermore the LGD is the loss severity observed during periods of high credit losses „distressed periods‟Source: US Federal reserve system, Federal register Vol.69, 2004 notice” However the above mentioned practice is a US GAAP requirement, but isn‟t according to the CBE GAAP, rather it would be considered as a conservative approachPage 86 Market practice series “Credit losses modeling”
  87. 87. FAQPD calculation:1. How should the withdrawn ratings be treated? The „withdrawn ratings‟ is observed when an obligor has a risk rating at the beginning of the period but eventually no risk rating by period-end „due to settlement‟ of the credit exposure An approach being adopted by S&P is to adjust for the withdrawn ratings by subtracting all their exposure from the denominatorNote that the withdrawn accounts are treated in adverse to the sold exposures.The difference in the treatment can be reasoned by the fact that the withdrawn account, proved to be able to settle itsexposure, and the risk model‟s objective is to measure the risk of „loss severity‟.Page 87 Market practice series “Credit losses modeling”
  88. 88. FAQPD calculation … continued:2. How should the new credit exposure that arrive in the middle of the period be treated? There are two approaches: A. Consider the mid-period credit line as an observation, That‟s to embed in the calculation of the PD, the balance of that observation in the nominator, and the balance of the credit line at time of initiation in the numerator. B. Consider the mid-period credit line “not” as an observation. That‟s to ignore the value of the credit line in the calculation of the PD, thus the PD shouldn‟t get impacted by the change in the mid-period credit line initiations.Page 88 Market practice series “Credit losses modeling”
  89. 89. FAQCCF calculation:► There are instances when the borrower have settled a portion of the outstanding loan, resulting in a negative CCF%, how should it be treated? e.g., a borrower has been granted a credit card with a limit of $150, as of period 1 the total due balance is $100, however in period 2 his due balance has been $75 due to settlement, then CCF would be: Available limit $50= ($150-$100) period 1, Available limit $75= ($150-$75) period 2, CCF -50%= ($50-$75)/$50. The negative CCF% should be eliminated from the calculation of the average CCF%. An alternative solution is to calculate the CCF only for the increased credit lines, rather than for a total portfolio with an offset impact of both draw-downs and settlements; means negative CCF per borrower should be eliminated.Page 89 Market practice series “Credit losses modeling”
  90. 90. FAQLGD calculation:► There are instances when the LGD is negative or some other instances when it‟s very highly positive, how should it be treated? A negative LGD (1-R.Rate) usually comes from the fact that the recovery rate is over 100%, which is mainly attributable to higher collateral value or more cash settlement for the due loans. However the highly positive LGD „being above 100%‟ comes from the fact that there were additional lending to the default loans whether in form of support to help the borrower meet its short term dues or in a form of agreement to postpone the loan settlement, thus accruing more fees and interest. Whatever the cause is, there has not been specific guidelines in this regard, however the market practice has been; flooring the negative LGD to -10% and capping the positive to 175%, and another market practice has been; flooring the negative LGD to 0%, and capping the positive LGD to 105%.Page 90 Market practice series “Credit losses modeling”
  91. 91. FAQSecuritized loan calculation:► How should the credit losses of the securitized loan portfolio be measured? It should be noted that a securitized loan portfolio should be subject to the same basis of calculation of the risk components (EAD „CCF‟, PD, LGD) to the extent the originator “seller” has retained an interest in the securitized loan portfolio; thus for banks with a regular history of securitization or sell-off, especially securitizing loans of particular type „mainly poor performing loans‟, reference data sets should be available from the trustee, or loan servicer. Alternatively, refer to the data sets for the retained pool of loans.Source: US Federal reserve system, Federal register Vol.69, 2004 notice”.Page 91 Market practice series “Credit losses modeling”
  92. 92. Data requirements
  93. 93. Data requirementsExample of the data requirements► Hereby we list an example of the data requirements, subject for use under any of the previously mentioned methodologies, whether under the historical charge-off method or under the migration analysis methodologies► Specific requirements should be customized to each methodology by its own, based on its risk componentsPage 93 Market practice series “Credit losses modeling”
  94. 94. Data requirementsCorporate loan portfolio:1. Direct exposure: Performing & Non-performing (customer ID, name, total outstanding, deferred fees, accrued interest, risk rating, loan type (Term, revolving), credit limit for the revolving lines, tenor for the term loans, collateral type, collateral value, interest rate, sector, branch #, Pastdues in value, pastdues in days, pastdues in number of installments), ► If any; (restructuring date, restructured value, tenor before restructuring, modified tenor), especially for the customers who either are not identified as a default loan, or as a restructured loan, or as a pastdue loan. ► Additional data for the non-performing loans; (time of default, recoveries made in value, source of recovery „guarantee/collateral/asset liquidation‟, recoveries in dates, charge-offs in value, charge-offs in dates) ► Obligor pricing model as of the date of assessment „interest rate that compensate for the credit risk‟ to determine the yield spread & discount ratePage 94 Market practice series “Credit losses modeling”
  95. 95. Data requirementsCorporate loan portfolio:2. Indirect exposure: Performing & Non-performing (customer ID, name, total outstanding, deferred fees, risk rating, credit line type (Term, revolving), credit limit for the revolving loans, collateral type, collateral value, interest rate, sector, branch #, Pastdue fees, pastdue fees in days, liquidation date, expiry date, reason of default if any)3. Covered Period: Data for at least 5 years backward, with an appropriate time-horizon.Page 95 Market practice series “Credit losses modeling”
  96. 96. Data requirementsRetail loan portfolio:1. Direct exposure: Performing & Non-performing (customer ID, name, total outstanding, deferred fees, accrued interest, product type, credit limit for the revolving lines, collateral type, collateral value, interest rate, sector, corporate employer, branch #, Pastdues in value, pastdues in days, pastdues in number of installments, geographical location) ► Additional data for the non-performing loans; (time of default, recoveries made in value, source of recovery „guarantee/collateral/asset liquidation‟, recoveries in dates, charge-offs in value, charge-offs in dates) ► Obligor pricing model as of the date of assessment „interest rate that compensates for the credit risk‟ to determine the yield spread and discount rate2. Covered Period: Data for at least 5 years backward, on quarterly basis or semi-annual basis.Page 96 Market practice series “Credit losses modeling”
  97. 97. Thank you

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