Credit Risk Management

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1 Synopsis Credit Risk Management 1. Introduction Learning Objectives This module introduces the key ideas for managing credit risk. Managing credit risk is a complex multidimensional problem and as a result there are a number of different approaches in use, some of which are quantitative while others involve qualitative judgements. Whatever the method used, the key element is to understand the behaviour and predict the likelihood of particular credits defaulting on their obligations. When the amount that can be lost from a default by a particular set of firms is the same, a higher likelihood of loss is indicative of greater credit risk. In cases where the amount that can be lost is different, we need to factor in not just the probability of default but also the expected loss given default. Determining which counterparty may default is the art and science of credit risk management. Different approaches use judgement, deterministic or relationship models, or make use of statistical modelling in order to classify credit quality and predict likely default. Once the credit evaluation process is complete, the amount of risk to be taken can then be determined. After completing this module, you should: understand the nature of credit risk, and in particular: what constitutes credit risk the causes of credit risk the consequences of credit risk understand the nature of the credit assessment problem, and that: credit risk can be viewed as a decision problem the major problem in assessment is in misclassifying credit risk understand the different techniques used to evaluate credit risk, namely: judgemental techniques deterministic models statistical models be able to set up and undertake the credit review process know the basic contents of a credit policy manual. Sections 1.1 Introduction 1.2 Credit Assessment Methods Credit Risk Management Edinburgh Business School 1

2 Learning Summary 1.3 Expected Losses and Unexpected Losses 1.4 Controlling Credit Risk 1.5 The Credit Policy Manual This module has introduced the key concepts for managing credit risk. Credit risk arises from changes in the financial solvency of firms and individuals. An event of default occurs when the obligor fails to perform under the terms of the contract. In this case, the lender or party with the credit is exposed to a potential or actual loss. The degree of loss will depend on how much can be recovered given the credit event or default. Many factors affect the potential exposure to credit events and hence creditrelated losses. The key element in determining the acceptability of risk taking in regard to credit exposures is in assessing the probability of default. This involves analysing and assessing counterparties based on a variety of techniques. Even so, there is the potential for exposure to unexpected and at times catastrophic losses from credit events. For this reason, firms need to control these credit risks through setting out policies on evaluation, management and having the correct procedures in place. Introduction Credit risk is the risk of loss from exposure to firms that undergo credit events. This might be that the obligor defaults, but in some cases it is that adverse changes in credit quality can lead to losses. There are a great many events that can have a credit impact, which complicates the definition, analysis and management of the process. Credit risk can be seen as an informational problem. The credit giver does not know enough about the quality of the credit taker and how the obligor will perform in the future. As a task, credit risk management involves identifying the source of risk, selecting the appropriate evaluation method or methods and managing the process. This will mean setting an appropriate cut-off point that balances the conflicting demands of the organisation with regard to credit exposure. Credit risk management can be seen as a decision problem. The assessment involves determining the benefit of risk taking versus the potential loss. Decisions about extending credit are complex and subject to change, but at the same time are critical elements of risk control within most organisations. While it is easy to outline the credit analysis decision, implementing an effective approach is more complicated. At its simplest, it requires an assessment of the likelihood that a particular counterparty will default on a contract and of the loss given default (LGD). As a process, credit decisions usually involve some classification of creditworthiness into categories or classes as a precaution against credit exposure to high-risk Credit Risk Management Edinburgh Business School 2

3 counterparties. This allows new credits to be analysed by comparison to preclassified credits whose default history is known. Credit Assessment Methods Credit appraisal can involve a number of techniques that can be used individually but are more often combined as part of the assessment process. These techniques can be categorised as either qualitative or quantitative in approach. There are basically three separate methodologies: judgement, deterministic models based on past experience or knowledge of the risks, and statistical models that may be either static or dynamic, or involve monitoring behaviour over time. Two basic methods exist for analysing credit quality: traditional quantitative qualitative credit analysis and decision models based on deterministic or statistical processes. Each offers a different insight into the credit risk problem. Expected Losses and Unexpected Losses In many cases, as with financial institutions, the amount of credit given by an organisation is substantial and requires steps to control the exposure in order to prevent unanticipated losses emerging. Unanticipated losses arise due to the variability of loss rates experienced over time, for instance as a result of changes in business conditions. If the loss experience in practice is above that expected, organisations will experience unexpected losses over and above those anticipated. This will happen as a result of variability in the actual loss rate against the expected loss rate. In some cases losses may be catastrophic, in that they far exceed any reasonable degree of variation that historical loss experience would indicate. Such losses can have a disproportionate effect on the organisations subject to such a risk. Controlling Credit Risk The credit analyst or manager is required to understand the ways in which bad debts or credit losses arise and to devise methods for identifying these. This then requires that due consideration is given to how these are effectively managed. A key issue is credit control, which involves constantly managing the creditgranting process. This can be seen as a policy that includes procedures, guidelines and processes for managing the credit process. Diversification can play an important role in reducing exposure to unexpected and catastrophic losses. However, spreading risks will be effective only if the principles of efficient portfolio construction are followed. There is a danger that the portfolio is ill-diversified, leading to unexpected losses. As with all risk management processes, the exposure to credit risks has to be kept under constant review and action taken as required. Credit risk management is a dynamic process that responds to new information. Finding the links between a firm s financial condition, behaviour and default is the key skill required in the management of credit or counterparty risk. Credit Risk Management Edinburgh Business School 3

4 The Credit Policy Manual This process of credit risk management is formalised in most organisations in a set of procedures generally called a credit policy manual. 2. Understanding Financial Statements Learning Objectives Accounting numbers are the language of business. They are the means by which an organisation, firm or company records its activities (that is, the transactions it undertakes). The accounting record is not only a record of its activities. In presenting a set of accounts, the company presents a position statement, known as the balance sheet, which gives a snapshot of its position at a given point in time, and an income statement (also known as a profit and loss account), which provides a record of its trading activities over the reporting period. The reported financial statements are summaries of the individual transactions made by the company over the reporting period. These are built up from the bookkeeping ledgers or accounts that track and record individual transactions. The reported financial statements can be supplemented by a cash flow statement, which shows the movement of cash within the firm. The presentation of accounts is based on conventions and reporting standards. The recognised global method is called the International Financial Reporting Standards, but alternative ways of presenting the same underlying activity are also used. This can lead to differences in presentation that can mask the true and fair view requirement criteria for published accounts. At its extreme it can lead to creative accounting designed to mask the true underlying state of affairs of the company. After completing this module, you should: understand how a company s transactions are recorded using accounting processes understand the mechanics of double entry bookkeeping used by firms know how transactions are recorded in accounts and how increases and decreases in items affect different ledgers be able to see how financial statements provide summary statements of the underlying transactions entered into by the firm know the elements that go into making up the balance sheet and income statement know the elements that make up a set of financial statements, namely: the balance sheet the income statement the cash flow statement be able to prepare a simple set of financial statements Credit Risk Management Edinburgh Business School 4

5 be able to create a simple cash flow statement from the balance sheet and income statement understand the principles, conventions and standards used to prepare a set of financial statements be aware of the problems a user of financial statements might experience due to the use of different approaches in preparing the accounts, namely: treatment of depreciation income recognition accounting for research and development expenditure treatment of goodwill and other issues that affect the quality of reported financial statements. Sections 2.1 Introduction 2.2 Double Entry System 2.3 Reported Financial Statements 2.4 Problems with Financial Statements Learning Summary Introduction Accounting is the language of business and records the transactions, assets and liabilities of an enterprise or organisation over a designated period. The accounting equation, which states that assets equal capital and liabilities, is the foundation of the accounting method and dictates the presentation of accounting numbers. It is also important in understanding financial statements. Accounting numbers relate to two elements: stock measures, which are of a permanent nature, and flow measures, which are transient elements. Capital is a stock measure; sales is a flow measure. Every business transaction leads to new entries in the accounting system and hence affects the resultant financial statements. These will be recorded either in the balance sheet, which presents the operations of the enterprise at a given point in time, or in the income statement (also called the profit and loss account), which presents the flow of transactions over the reporting period. A third statement, called the cash flow or flow of funds statement, can also be prepared or derived from the balance sheet and income statement. Double Entry System The double entry system is the way firms operate the accounting process. It involves creating ledger accounts, or T accounts, for each category of transaction. The key element, which gives the process its name, is that transactions are entered twice in the ledger system. That is, each transaction generates two entries, in different ledgers. Credit Risk Management Edinburgh Business School 5

6 For each ledger account, there are two sides: the debit or left-hand side and the credit or right-hand side. Placed side by side with the name of the account (for instance, cash account ), the organisation is in a T shape, which leads to these accounts being called T accounts or ledgers. The double entry system means that, for assets, an increase in assets is entered on the debit or left-hand side and a decrease in assets is entered on the credit or right-hand side. For liabilities and capital, an increase in liabilities is entered on the credit or right-hand side and a reduction in liabilities is entered on the debit or left-hand side. This preserves the concept of sources of funds (credits) and uses of funds (debits) for both assets and liabilities. Given the double entry system and the existence of the relevant accounts that record the operations of the firm, at the reporting date it is possible to manipulate these entries into a set of financial statements (that is, a balance sheet and income statement). There is normally an intermediary stage where a trial balance is created before the completion of the balance sheet. It is also possible to create a cash flow or sources and uses of funds statement. Reported Financial Statements For most users of accounts, what they are presented with are reported financial statements, which may or may not include additional disclosure information. The basic set of financial statements, which may be audited or unaudited, comprises a balance sheet at the reporting date and an income statement covering the reporting period. Additionally, there may be a cash flow or sources and uses of funds statement and segmental information about the operations of the firm. In preparing financial statements, accountants will adopt a number of principles and conventions. These are prudential treatment of reported items, neutrality, completeness, faithful representation, historical cost convention, accrual accounting, matching principle, no offsetting of transactions, materiality and aggregation of items, going concern concept, substance over form and consistency of presentation. These principles and conventions govern the way financial statements are presented and what information is provided. The key element is that they are prepared on a conservative basis according to defined rules. Any set of financial statements will have notes that explain the principles that were used in drawing up the accounts. Financial statements and reports are prepared according to generally accepted accounting principles of the jurisdiction in which the firm operates. Increasingly, large firms in a given country will report using IFRS. The balance sheet is a statement of the financial position of the reporting entity at a given date. It presents the total asset and liability position broken down by category. For assets, the principal categories are non-current assets and current assets. For liabilities, the principal categories are equity or shareholders funds, long-term liabilities and current liabilities. Presentation of the balance sheet can be in accordance with the so-called sideby-side method, which has assets on the left-hand side and liabilities on the Credit Risk Management Edinburgh Business School 6

7 right-hand side, or in accordance with the netted-down method, which shows the funds employed in the business. Both methods have their advantages. The income statement shows the revenues and expenses of the firm over the reporting period. This is made up of three elements: a trading statement, overhead elements for supporting the business and how profit (or loss!) is allocated. The third reported financial statement is the cash flow statement, which is also called a uses and sources of funds statement. This shows how cash was generated by the firm and where that cash was used. Note that, unlike the balance sheet and income statement, this statement does not allow for accrual accounting since it shows the cash movements within the firm. In principle, the net cash flow over the reporting period should equal the change in the firm s cash position (either a decrease or increase in existing cash balances or borrowings). Most large firms will provide additional information, breaking down operations by business activity and/or geography. Since audited financial statements are a statutory obligation for firms, annual reports also include considerable additional information mandated by law on the operations and activities of reporting firms, which is a useful supplement to the accounting numbers themselves. Problems with Financial Statements A number of problems arise in the preparation and presentation of financial statements that users of such information need to be aware of. Different generally accepted accounting principles in use across the world can lead to differences in reported accounting numbers. The key problems relate to income recognition and the treatment of expenses. Depreciation, which is an accounting expense to reflect the loss in economic value of assets, can be handled in different ways and hence provide different results depending on the method used. To make matters worse, accounting depreciation is different to depreciation for tax purposes. When to recognise an item as revenue, an asset or a cost can also be problematic. A major issue arises with research and development expenditure and whether to treat it as a cost (in which case it should be expensed) or as an investment (in which case it can be capitalised as an asset and subsequently depreciated). The treatment of goodwill following an acquisition can also be problematic and differences in approach can lead to very different accounting results. The accounting treatment of acquisitions, namely whether it is done through acquisition (purchase) accounting or merger accounting methods, can lead to very different financial results. At its extreme, the difference in methods allowed under generally accepted accounting principles can lead to creative accounting, or window-dressing of the financial statements, with the intent to deceive or at the very least present the firm s activities in the best possible light. Where legitimate accounting treatment stops and creative accounting starts is unclear, but users of financial statements need to be aware of the potential to mislead in the way some firms report their activities. Credit Risk Management Edinburgh Business School 7

8 Recent moves to International Accounting Standards are designed to minimise the scope for creative accounting and to set a global benchmark for reporting standards, known as International Financial Reporting Standards, and which will be used by firms across the globe. 3. Ratio Analysis Learning Objectives Financial analysis is the process of examining the financial statements of a firm with a view to understanding the nature, activity and risks that are inherent in the business. Financial statements provide a condensed summary of a firm s activities. The balance sheet shows the assets and liabilities that are used to make the business function. The income statement shows what revenue was earned and how it was used during the course of the reporting period. The relationships that exist between different accounting entries provide useful information on the nature of the firm s activities. Ratio analysis is the process of using accounting and other business numbers to extract useful information for evaluation purposes. Ratios allow firms to be compared across time and against their industry or sector. Ratio analysis allows the analyst to understand the underlying risks in the business and, in particular, to ascertain the amount of credit risk. After completing this module, you should be able to: create common-sized financial statements for comparing two firms understand the key concepts of ratio analysis and, in particular, understand the different types of ratio that can be created make use of available information to create ratios know how different ratios are calculated perform ratio analysis on any two items in a set of financial statements understand the different types of ratio that are used in ratio analysis, namely: activity, efficiency or management ratios liquidity or solvency ratios operating or management ratios profitability ratios leverage or gearing ratios market-based ratios recognise the key ratios used by most financial analysts for evaluating a set of financial statements understand the interconnections that exist between ratios, and in particular the concept of the hierarchy of ratios that allows a detailed examination of a firm s performance undertake trend analysis using ratios Credit Risk Management Edinburgh Business School 8

9 undertake inter-company, sector or industry analysis using ratios understand the analytic relationship model approach to using ratios for evaluation purposes and, in particular, the DuPont model. Sections 3.1 Introduction 3.2 Ratio Analysis 3.3 Using Ratios 3.4 Analytic Relationship Models Learning Summary Introduction The starting point for ratio analysis is the use of financial statements as discussed in Module 2. These provide a detailed summary of a firm s activities. The balance sheet shows the position at the financial year-end, while the income statement provides an explanation as to what income was earned and how it was used during the reporting period. Ratio analysis allows a thorough examination of a firm s accounts to provide an understanding of the nature, activity and risks that are inherent in the business as reported in the accounting numbers. Ratios allow firms to be examined both across time and against their industry peers or activity sector. A key component of any risk assessment involves financial analysis. Ratio analysis allows comparisons between firms and the same firm across time by removing the size effect. One approach is to use common-sized financial statements, but they have limitations. The solution is to create ratios using two or more accounting numbers. This is a more flexible approach that allows the analyst to understand how a firm operates. Ratio Analysis Ratio analysis means dividing a numerator value by a denominator value to create proportions, percentages or multiples. These facilitate comparisons between items and across firms and time. Any information available to the analyst can be used for creating ratios; this includes non-financial information (for instance, number of employees, market price of a firm s securities and so forth), although there should be a logical case for the ratio being created. Ratios are normally created to answer questions about a firm s performance, risk, operations, financial leverage or other aspect of the underlying economic activity being performed. Ratios are normally classified into different types, namely: activity, efficiency or management skill Credit Risk Management Edinburgh Business School 9

10 liquidity or solvency operational efficiency or management efficiency profitability financial leverage or financial gearing market-based ratios In practice a relatively small number of key ratios that address the issue of firm performance, profitability and so forth have become accepted for analytic purposes. Key activity ratios include average collection period, inventory turnover and asset turnover or total asset turnover. Key liquidity ratios include current ratio and acid test (quick) ratio. Key operating or management performance ratios include gross profit margin, net profit margin and free cash flow (generation). Key profitability ratios include return on capital employed, return on investment and return on equity. Key risk ratios include debt, leverage or gearing ratios, interest cover and fixedcharge coverage. Key market-based ratios include price to earnings multiple, market-to-book and net asset value. Other ratios can be created to suit particular types of firms being analysed; for instance, in natural resources firms it might be profit per tonne or unit produced. In principle, there is no limit to the types and diversity of ratios that can be created. In practice, many ratios are very similar and hence only a small number are usually required to give a complete understanding of the character of the firm being analysed. Ratios are interconnected in a logical structure, called the hierarchy of ratios, with sub-ratios feeding into higher-order ratios. Ratios are used for trend analysis (observing the same ratio over time) or for cross-sectional analysis of the firm against other similar firms, the industry or sector. While a useful tool, there are limitations in the approach, especially when applied across firms. Analytic Relationship Models Given the interconnection between ratios and the hierarchy of ratios, it is possible to create analytic relationship models using ratios for evaluating the performance and behaviour of firms. The best-known analytic relationship model is known as the DuPont model, after the firm that first developed its use. There are two elements to this model: the underlying business performance, measured by the return on assets, and the effect of financial structure, measured by the return on equity. By using ratios, it is possible to see the contributor elements of each component when using the DuPont model. Credit Risk Management Edinburgh Business School 10

11 4. Expert and Rating Systems Learning Objectives Sections This module examines how credit quality is determined using expert and judgemental methods. These approaches to credit risk assessment are subjective and largely rely on the experience of the analyst. As such, they represent the traditional method of credit analysis, which seeks to compare one credit with another in order to grade and rank the credit in terms of quality. How these rankings are determined involves a mixture of process and considered opinion and hence they lack transparency and rigour. That said, they are in common use as a means of credit assessment. After completing this module, you should: comprehend the process by which judgemental credit assessments are made be able to distinguish between formal models and expert judgement approaches to credit risk assessment understand how expert judgement credit evaluation is not a single technique but a set of different methods that includes: qualitative assessments relationship models comparator credit rankings behavioural models be able to integrate more than one expert and scoring technique understand the template or checklist approach to subjective credit assessment and in particular know the meaning of the 6 Cs of credit; that is, a credit s: character capacity capital collateral conditions compliance be able to undertake simple credit ranking procedures and be able to carry out simple credit scoring understand the credit rating process and the meaning of the credit ratings given to firms by credit rating agencies such as Moody s Investors Service and Standard & Poor s comprehend the underlying rationale for behavioural scoring and in particular know the approach used in the A-score model developed by John Argenti. 4.1 Introduction 4.2 Credit Evaluation 4.3 Qualitative Credit Assessment Processes 4.4 Credit Ranking Credit Risk Management Edinburgh Business School 11

12 Learning Summary 4.5 Behavioural Ranking Introduction Expert and ranking systems involve subjective judgements about credit quality combined with templates, processes, checklists and other decision aids in order to determine credit quality. Such systems are inherently less transparent than formal systems and subject to analytical bias and selection. Subjective judgement based on the past experience of the assessor has been the traditional method for analysing credit and continues to be used as part of the credit assessment process. It needs to be contrasted with more formal methods that model relationships between the firm (or individual) being analysed and credit quality. Expert judgement and ranking processes follow a logical decision-making process, although not a formal one, in that the problem is first defined and then there follows analysis in order to be able to make the appropriate decision. For credit risk assessment, the decision is whether the credit is a good one (and credit can be advanced) or a bad one (and credit should be refused). More sophistication is achieved by ranking, where the best-quality credits are allowed more credit than poorer-quality credits. Credit Evaluation Credit evaluation can take a number of different forms. The major distinction is between subjective models, which include expert judgement and ranking procedures, and formal models, which make use of known relationships to determine credit quality. The principal types of model that are in use include: qualitative models or expert judgement models where the appraisal is based purely on subjective judgement relationship models where analytic relationships are used to determine the quality of the credit credit ranking where a credit is compared to existing credits whose quality or rank is known using a set of given criteria and analytic relationships behavioural models, which take as their basis the actions of the firm s managers Assessments often make use of multiple methods of analysis, and insights gained from one type of method are used to illuminate the results from other methods of evaluation. Qualitative Credit Assessment Processes Qualitative credit assessments involve judgements by analysts. To assist in the process and to act as a template, many such assessments use a set of elements to subjectively determine the credit quality of the entity being assessed. Credit Risk Management Edinburgh Business School 12

13 A common model of credit assessment is the 6 Cs of credit. These are: character: the personal characteristics of the borrower capacity: the past behaviour and prospects of the borrower capital: the amount of capital, equity or own funds supporting the borrowing collateral: assets pledged or available to support the borrowing conditions: the economic backdrop to the credit request compliance: whether the borrowing satisfies regulatory and legal requirements Problems can arise with credit situations that need to be investigated as part of the credit evaluation process; in particular there may be compliance problems with types of credits and transactions that affect the credit assessment. These include ultra vires (that is, the borrowing is beyond the competency of the borrower to undertake), the unsuitability of the transaction and foreign counterparties. Credit Ranking Credit ranking is a judgemental technique that applies numerical values to elements of a credit s financial condition, character, collateral and so on in order to score the result as part of the credit assessment process. While more formal than a simple credit assessment, the process is still nevertheless judgemental in approach as the analyst places their own estimate on the variables being evaluated. Ranking models seek to classify credits into groups that share common credit risk characteristics and that can be treated as equivalent for decision-making purposes. Credits are ranked from best to worst using a scale or criteria for determining the appropriate credit class for the entity being evaluated. Ranking methods can be applied to all aspects of a business. So while it is possible to rank the financial condition of a firm (by analysing its financial statements and other financial information), it is also equally feasible to rank or score other aspects of a firm s activities, such as its competitive position and strategy, the quality of its management, the superiority of its technology and know-how, and other qualitative aspects of the business s activities. In undertaking ranking procedures, qualitative elements are often given numerical value using a subjective scaling system such as the Likert scale. Analysts will seek to reduce their judgements about the credit quality of a company down to a single number with a clear interpretation. This is called rating and is a combination of objective criteria and subjective assessment of the creditworthiness of the entity being rated. Rating agencies such as Moody s Investors Service or Standard & Poor s provide credit opinions and rank corporate and governmental entities into credit classes on the basis of judgement (called an opinion) that reflects their creditworthiness. Credit Risk Management Edinburgh Business School 13

14 Behavioural Ranking Behavioural ranking extends the principles behind rating methods to include observable behavioural characteristics of firms managers on the principle that it is the actions of managers that determine the success or failure of the firm. Causes of corporate failure include problems with a firm s markets and products, poor-quality management, poor investment and acquisition decisions, and poor internal management of the firm s activities. Some of the factors are susceptible to financial evaluation (for instance, using ratio analysis) while others can be scaled, but determining the scale of the factors is a judgemental decision. The approach looks for qualitative signs that in the view of the analyst are a cause for concern. In particular, it examines poor corporate governance (that is, how the firm is directed and how well outside parties are kept informed through financial reporting) and any signs of problems. Using the above approach, John Argenti developed an approach to corporate assessment that focused on behavioural aspects called the A-score. 5. Credit Scoring and Modelling Default Learning Objectives The previous module examined qualitative approaches to credit risk assessment. This module extends the approach and looks at a range of systematic methods that make use of statistical inference to determine credit quality for both firms and individuals. Models that are used to evaluate corporates are generally called defaultprediction models, whereas models used for consumer credit assessment are usually referred to as scoring models. While the data used for the two is generally different, the aim of the model is the same: to find a statistical basis for predicting credit quality. The principle underlying these models is that past behaviour or condition is a suitable guide for future behaviour. Statistical models seek to determine the best explanatory relationship between behaviour and a set of significant predictor variables. Such models provide a mathematical equation (which in use is called a scorecard) that provides a statistical estimate of the credit risk of the individual or firm being analysed against a known sample. A new credit is scored on the basis of the predictions of the model using the same variables that were used to develop the model. After completing this module, you should: understand the basis for the statistical modelling of credit know the difference between the judgemental or expert approach and the advantages and disadvantages of the systematic approach to credit modelling be able to construct a simple scorecard using the results of a statistical model for credit assessment Credit Risk Management Edinburgh Business School 14

15 understand the statistical basis for credit modelling, including the decision theoretic approach be able to undertake a simple discriminant analysis understand the meaning of the significance statistics used in regression analysis be able to compute the efficiency and error rate of the statistical model be able to comment on the issues related to the statistical modelling of credit know the differences between the various models used for statistical analysis understand the issues relating to the statistical modelling of firms and individuals be familiar with the different types of consumer credit scoring as well as other uses for the scoring approach be aware of the issues relating to consumer credit scoring and what variables are used in a credit score be aware of the advantages and limitations of the statistical credit appraisal and default prediction methods. Sections 5.1 Introduction 5.2 Statistical Basis for Modelling Credit 5.3 Applying Scoring Models to Firms 5.4 Consumer Credit Scoring 5.5 Behavioural Scoring Models 5.6 Advantages and Limitations of Credit Appraisal and Default Prediction Methods Learning Summary Introduction The systematic approach used for credit scoring and modelling default relies on statistical inference using a set of predictor variables to determine whether a credit is good or bad. The use of such a formal model reduces the credit evaluation to a data-processing exercise that makes it very suitable when there are a large number of cases, each for a relatively small amount. Formal models avoid the problem of judgemental bias and allow the decision maker to set the cut-off point for accepting the credit risk. While profit maximisation is a desirable objective, it is often replaced with minimising classification errors (that is, determining a bad credit as good). In use these models become a scorecard where a small number of key variables is used to evaluate the creditworthiness of the applicant individual or firm. Statistical Basis for Modelling Credit The statistical modelling of credit risk is, in essence, a discriminatory approach whereby a dataset of past cases is used to develop a model that best separates good credits (that is, credits whose track records lead the decision maker to clas- Credit Risk Management Edinburgh Business School 15

16 sify them as performing credits) from bad credits (that is, credits who have defaulted or otherwise breached the terms of the credit). The models involve a decision theoretic approach to the credit evaluation problem, which involves developing a model whose decision rule is to minimise the expected loss from advancing credit. A good model will successfully discriminate between a good and bad credit applicant by comparing key information on the applicant to the scorecard. In the context of the model, the score or result is an estimation of the credit quality of the applicant. A linear probability model or regression model uses a linear function of predictor variables to explain the dependent variable. This dependent variable will be a categorical variable that usually takes a value of 1 if the case defaulted and 0 if there was no default. The predictor variable or variables is known information, possibly transformed in some way into a ratio or otherwise scaled (for instance, by using a log transformation), that provides the best linear explanatory equation. A linear equation will have the general form Y = a 0 + a 1V a nv n, where Y is the dependent variable (with a value of either 1 or 0) and V 1 is the ith predictor variable. The values a 0 n are the coefficients of the linear model. A statistical model will allow for various statistics to indicate the degree to which the equation provides a satisfactory degree of statistical fit and significance of the regression, namely: regression coefficient hypothesis, which tests the significance of the coefficients used in the model analysis of variance, which tests the amount of reduction in the error from the regression equation adjusted coefficient of determination, which measures the fit of the regression taking account of the impact of all the independent variables (when there is only one predictor variable, then the coefficient of determination is not adjusted). In practice, a user of a statistical scoring model will be concerned with how the model correctly and incorrectly classifies cases. The higher the percentage of correctly classified firms, the better the predictive power of the model. In addition, the degree to which the model misclassifies bad firms as good and good firms as bad will also have an important bearing on the validity of the model. Usually, minimising the number of bad firms classified as good and hence eligible for credit if the model is used for determining the decision will be an important feature of the model s effectiveness. Usually a model will require a number of predictor variables to minimise errors and achieve the desired level of predictability (that is, it will be a multivariatetype model). Developing and testing such models becomes an important task. There are a number of practical issues that arise with the use of statistical credit scoring techniques: Credit Risk Management Edinburgh Business School 16

17 There is no theoretical basis for the selection of predictor variables, and model developers rely on data-mining techniques. The choice of variables may be dictated by what is available to the model developers and the need to comply with the relevant legislation on consumer credit and lending. Many variables will be qualitative and will have to be transformed into quantitative variables. Any scoring model must meet or match the credit-granting organisation s lending policies. Sampling and other statistical issues arise when a model is being developed, as well as an understanding of the lending and other conditions from which the sample was drawn. There is a need to formalise many aspects of the data, including what constitutes a good and bad credit. In practice, a range of models is used, including logistic regression and Probit and Tobit models. Applying Scoring Models to Firms There is much more information available on firms. Scoring models for firms, which are generally bankruptcy prediction models, make use of accounting and other information as part of the information set. The principal models are known as Z-score models and make use of accounting data, which is often transformed into ratios, for their predictor variables. A key issue that arises with these models (and that is not absent from consumer credit scoring models) is the need to calibrate the model for the type and location of the company being analysed. Hence specific models need to be developed for individual countries and types of businesses. In practice, default prediction has been most useful for analysing small and medium-sized enterprises, where there is a relatively high likelihood of firms defaulting and where lenders have a large database of prior defaulted companies that can be used to develop the model. Consumer Credit Scoring Consumer credit scoring makes use of information easily obtained from individuals at the point of application. This is now the predominant method for determining whether an individual is eligible for credit and may be the only evaluation undertaken on small consumer credit transactions. Factors such as an individual s past payment history, amount outstanding with lenders, length of credit history, new credit and types of credit already advanced form part of the information used to analyse an individual s creditworthiness. Scoring models have been developed for a range of credit situations, including mortgage lending, collections, student loans, mobile home loans, detecting fraud, direct mail marketing and tax inspection. Credit Risk Management Edinburgh Business School 17

18 Behavioural Scoring Models Behavioural scoring models use information from an existing credit to predict future behaviour. As with application scoring, a range of information is available on the individual or firm, which is then used to predict future outcomes. The principal difference between application scoring and behavioural scoring is that, in the latter case, there is transactional information available to develop the scorecard. A key issue with any scoring model is the problem of reject inference. Since the data sets used to develop the model are all based on prior accepted credits, using these to predict non-performance biases the results. Hence in developing a model there are major validation issues that have to be addressed. Advantages and Limitations of Credit Appraisal and Default Prediction Models Credit scoring has become the established means for determining consumer credit. The approach is cost-effective and allows for a formalised lending process, in which: The criteria on which the lending decisions are made are explicit. Decisions between cases are consistent since they rely on an objective model. Management is in control of the process and the amount of credit risk being assumed. The cut-off point for accepting credit can be altered without major changes to procedures (although the effect will be lagged since all past decisions will have been made at the previous cut-off point). While there are many advantages to statistical credit scoring, there are also some disadvantages and problems with the method, namely: The scorecard does not take customer profitability into account; nor does it address the problem of screening bias. Companies use creative accounting and window dressing of their financial statements in order to disguise the underlying state of affairs. The score is developed at one point in the business cycle and may not be appropriate or may mislead in different economic circumstances. The sample is based on past cases that may not be a good guide to new credits (for instance, if existing customers are used to score a new credit card). There is no good underlying theory that supports scoring models and the variables used to develop the scorecard. Individuals and firms change their behaviour and circumstances over time. Credit Risk Management Edinburgh Business School 18

19 6. Market-Based Credit Models Learning Objectives This module extends the credit risk analytic techniques to those that incorporate or make use of the prices in traded securities markets to assess creditworthiness. As such, the approach in this module extends firm-specific analysis in that the models make use of the prices at which transactions take place in the financial markets. Prices in these markets reflect the market s best estimate of the value of the securities and the underlying obligor s credit risk. That is, they incorporate the market s collective judgement about the security s credit risk that is embedded within the security price. The analytic tools in this and the next module have been developed to reveal this information and make use of it in determining credit risk and the probability of default. The models can be considered to fall into two kinds: those that aim to measure all credit risk (that is, any change from a firm s current credit status), which are covered in this module, and those models that measure only a credit s default risk. The module starts with an explanation as to how market prices can be used to reveal default expectations. This is extended into the option-theoretic approach in the next module. Differences in methodology and what is being modelled explain why there is a multiplicity of market-based approaches in use by credit assessors. It should be added that these techniques are both relatively recent and, because of their purpose, relatively complex in operation. After completing this module, you should: understand how the market price of financial securities is affected by changes in credit quality be able to recognise how the market credit risk models determine the credit risk from market information know how to calculate the default probability implied by the market prices of pure discount bonds be able to undertake simple calculations of default probabilities given bond prices and yields and recovery rates have the ability to calculate the spot or zero-coupon interest rates from financial market data be able to compute the implied forward interest rates embedded in zero-coupon interest rates know how to compute the forward prices of bonds for different credit classes as part of the estimation procedure for calculating a bond s credit value at risk be able to calculate the expected value and variance in value for a particular bond due to changes in its creditworthiness at a given future time horizon understand how to adjust the variance of the bond value for uncertainty in the distribution of future values from credit effects know how to calculate the credit value at risk of a simple two-asset portfolio, that is: Credit Risk Management Edinburgh Business School 19

20 be able to compute the expected value of such a portfolio be able to calculate the variance and standard deviation of such a portfolio under the assumption of no correlation and in the circumstances when there is correlation between the two constituents be able to determine the credit value at risk of the portfolio at a given confidence limit understand the problems of using parametric methods with credit risk, given its asymmetric characteristics understand the problems in modelling credit risk by using ratings transition matrices be aware of and be able to calculate the benefits of portfolio diversification understand how market credit risk models may be adapted to take account of economic factors know the differences in rating philosophy and credit risk assessment between the point-in-time and the through-the-cycle approaches. Sections 6.1 Introduction 6.2 Credit Risk Portfolio Model 6.3 The Economic Factors Model: CreditPortfolioView Learning Summary Introduction Market-based models are a recent development in credit risk management techniques and rely on insights from financial theory and practice. These new models are market in the sense that they rely on the information that is embedded in the transaction price of securities over time. The market prices of securities contain useful information about the prospects of the underlying credits. Among the values that are reflected in the price is the market s consensus estimate about the likelihood that the underlying credit will default or suffer a credit downgrade. Among the useful information that a market-based credit model can reveal from securities prices are hazard rates (i.e. the likelihood of a credit event occurring) and conditional default probabilities over time. For certain kinds of securities, such as bonds, it is possible to determine the market s estimates of future credit risk with some degree of precision. In order to do so accurately, one needs to know what the loss or recovery from the credit event will be. So the evaluation requires us to know not just the market prices (or yields) on securities, but also the estimated recovery rate involved. Hence, credit spreads include two unseen and changing elements. Credit Risk Management Edinburgh Business School 20

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