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Credit Risk Management Chapters 11 & 12

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Credit Risk Management uniqueness of FIs as asset transformers –What do we mean? –What type of risk do FIs incur due to this role? –What are important things FIs must do to deal with/reduce these risks? importance of sound banking system for economic health –Japan –US credit problems in 1980s and 1990s

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Credit Analysis all analysis should be geared to one decision –Does FI grant the loan or not? –stated loan policy –clear documentation –criteria not discriminatory –minimum credit standards –standard application forms

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Types of Loans Commercial and Industrial loans –maturities –uses –amounts syndicated loans –secured or unsecured –spot/loan commitment –importance of commercial paper

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Types of Loans Real Estate loans –mortgage loans and home equity loans –commercial vs. residential mortgage loans –ARMs Consumer loans –i.e., personal or auto loans –revolving loan –usury ceilings

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Real Estate Lending large secondary market forces standardization of applications major factors in accept/reject decision –applicant’s ability and willingness to repay –value of borrower’s collateral characteristics/standards used to assess requirements

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Real Estate Lending GDS (gross debt service) ratio - gross debt service ratio calculated as total accommodation expenses (mortgage, lease, condominium, management fees, real estate taxes, etc.) divided by gross income TDS (total debt service) ratio - total debt ratio calculated as total accommodation expenses plus all other debt service payments divided by gross income

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Credit Scoring expresses applicant’s credit quality numerically – removes some subjectivity helps FI manager: –numerically establish which factors are important in explaining default risk –evaluate the relative importance of factors –improve pricing of default risk –be better able to screen out bad loan applicants –be in better position to calculate any reserves needed to meet expected future loan losses

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Credit Analysis Consumer and Small Business lending Mid-Market Commercial and Industrial –firms with annual sales of about $5-$100 million –subjective and objective in evaluation

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Credit Scoring Models linear probability and logit models –use past data as inputs into model to explain repayment experience on old loans –relative importance of factors in explaining past repayment is used to forecast repayment probabilities of new loans Linear discriminant models –while above models project a value for expected probability of default if a loan is made, discriminant models divide borrowers into high or low default risk classes contingent on observed characteristics –Altman’s Z Z = 1.2X 1 + 1.4x 2 + 3.3x 3 + 0.6x 4 + 1.0x 5 X 1 = working capital / total assetsX 2 = RE / total assets X 3 = EBIT / total assetsX 4 = MV equity / BV LT debt X 5 = sales / total assets

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The KMV Model Banks can use the theory of option pricing to assess the credit risk of a corporate borrower The probability of default is positively related to: –the volatility of the firm’s stock –the firm’s leverage A model developed by KMV corporation is being widely used by banks for this purpose Banks can use the theory of option pricing to assess the credit risk of a corporate borrower The probability of default is positively related to: –the volatility of the firm’s stock –the firm’s leverage A model developed by KMV corporation is being widely used by banks for this purpose

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Return on Loan methods to calculate –ROA (return on assets) –RAROC (risk-adjusted return on capital) factors that affect FI’s return on loan: –The base lending rate on the loan (L) –The credit risk premium (m) –Fees earned as a result of the loan (e.g. origination fee and credit line fees) –Whether the borrower repays in full on time –The value of collateral and ease and cost of collections if the borrower defaults –The nonprice terms and conditions on the loan (other than fees): Origination fee (f) Compensating balances (b) Reserve requirements (R)

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ROA ROA per dollar lent by FI is ROA per $ lent is going to be greater than simple promised interest return on loan if b>0 because FI gets to keep compensating balance

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Example 1 A bank has a base lending rate of 8% (L), and charges a certain customer a 110 basis point risk premium (m). The bank also charges a 1% origination fee (f). The bank requires the borrower to maintain compensating balances of 7% of the loan amount. The reserve requirement is 10% and the loan amount is $1 million.

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Example 2 A corporate customer obtains a $1 million line of credit from a bank. The customer agrees to pay a 9% interest rate and agrees to make compensating balances of 6% of the total credit line and 3% of the amount actually borrowed. These will be held in non-interest bearing transactions deposits at the bank for one year. The bank charges a 1% loan origination fee on the amount borrowed and a 0.25% commitment fee on the unused line of credit. The expected draw down (loan amount) is 60% of the line for one year. Reserve requirements are 10%. What is the expected rate of return to the bank?

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Example 3 The credit risk premium (m) can be set based on historical default rates on loans of this category and rates of return on defaulted loans. For instance in order to earn the base loan rate of say 9% if the default history of a given loan category is as follows: % of loans Default experience RoR on category 98%No default 9% + m 1.5%Limited default 0%[1] [1] 0.5%Total writeoff-100% [1]The percent of loans and the rates of return numbers should both be net of recoveries [1]

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RAROC The risk adjusted return on capital (RAROC) originated by Banker’s Trust is now widely used instead of the ROA method of loan pricing presented above.

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Example 4 Continuing with Example 1 from above and adding the additional necessary information will illustrate how to calculate the RAROC: The loan had income of $101,000. Suppose the dollar cost rate (including interest and noninterest costs) of providing the loan is 10.3%. The net loan amount was $937,000 so the dollar cost is thus $96,511 (=$937,000 0.103). Suppose that typical default rates may be 0.3% in a given year. However, according to historical default rates, the 99th percentile, or the extreme loss rate, for this loan category is 3%.[1] This means that the bank believes that in the worst case scenario (which in this case should happen only once every hundred years), 3% of the loans will default instead of the typical 0.3%. [1] Suppose further that based on historical data the bank can expect to eventually recover 25% of the loans that default.

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Loan Portfolio Risk credit scoring and RAROC and other methods helped FI analyze risk of individual loan also need to measure credit risk to entire loan portfolio simple models of loan concentration risk –migration analysis –concentration limits

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Concentration Limits external limits set on the maximum loan size that can be made to an individual borrower set limits by assessing the borrower’s current portfolio, its operating unit’s business plans, its economists’ economic projections, and its strategic plans

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Example 5 Suppose management is unwilling to permit losses exceeding 10% of an FI’s capital to a particular sector. If management estimates that the amount lost per dollar of defaulted loans in this sector is 40 cents, the maximum loans to a single sector as a percent of capital, defined as the concentration limit, is CL = maximum loss as % of capital * (1/loss rate) = 10% * (1/0.4) = 25%

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