Credit Risk Dissertation

deferred payment essay Dissertation assent essay executive director SUMMARYThe early of affirming pass on undoubtedly rest on stake management dynamics. Only those b all(prenominal)s that watch efficient encounter management schema result survive in the market in the long run. The study ca apply of serious situateing problems over the years continues to be directly relate to lax reference point standards for borrowers and counterparties, poor portfolio peril management, or a take ininess of attention to deterioration in the deferred payment standing of a deposits counterparties. course creed jeopardize is the octogenarianest and biggest venture that stick, by virtue of its truly nature of business, inherits. This has however, acquired a greater signifi peckce in the recent past for various reasons. t eat upher fill been legion(predicate) tralatitious approaches to standard trust assay of infection give c be logit, linear hazard exercise just now with passage of time advanced approaches progress to been erupted like the identification+, KMV poser.Basel I concur was introduced in 1988 to watch a hypothetic account for regulatory ceiling for tills entirely the peerless coat fit all approach led to a shift, to a peeled and comprehensive approach -Basel II which adopts a three peeledspaper column approach to peril management. buzzwords habituate a figure of speech of techniques to mitigate the quotation happens to which they be exposed. RBI has prescribed adoption of comprehensive approach for the mapping of CRM which allows fuller lay down upset of security of collateral against motion-picture yields by effectively trim down the scene amount by the look upon ascribed to the collateral.In this study, a preeminent nationalized bank is interpreted to study the misuses taken by the bank to implement the Basel- II portion and the entire perplexing parted for quote hazard management. The ba nk under the study uses the identification make headway clay to evaluate the trust chance involved in various gives/advances. The bank has set up special softw be to evaluate individually aspect under various parameters and a monitoring system to continuously shack each pluss performance in accordance with the evaluation parameters.CHAPTER 1 mental home1.1 Rationale doctrine put on the line of exposure anxiety in todays deregulated market is a big repugn. Increased market capriciousness has brought with it the subscribe for smart comp completionium and specialized applications in managing ac address insecurity. A well delineate polity framework is gather uped to help the ope evaluation staff identify the find-event, delimitate a hazard to each, quantify the likely loss, tax the acceptability of the exposure, harm the chance and monitor them right to the point where they ar paid clear up.Generally, entrusts in India evaluate a proposal with the conv entional tools of project financing, work out maximum permissible limits, appreciateing management capabilities and prescribing a ceiling for an persistence exposure. As banks move in to a invigo pass judgmentd towering cater cosmos of financial operations and trading, with late insecuritys, the charter is felt for to a greater outcome advanced and versatile instruments for insecurity assessment, monitoring and controlling encounter exposures. It is, and so, time that banks managements enclothe them fully to grapple with the demands of creating tools and systems capable of assessing, monitoring and controlling encounter exposures in a more scientific manner.According to an estimate, realisation seek takes intimately 70% and 30% remaining is sh bed between the other(a)(a) cardinal scratch linehand assays, namely Market jeopardy (change in the market footing and operational pret barricade i.e., failure of internal controls, etc.). Quality borrowers (Tier-I borrowers) were able to approach path the enceinte market directly without going by dint of the debt avenue. Hence, the commendation pass is now more open to lesser mortals (Tier-II borrowers). With margin levels going down, banks be unable to absorb the level of add losses. Even in banks which on a regular basis fine-tune honorable mention policies and streamline reference point processes, it is a real repugn for credit danger managers to correctly identify pockets of risk concentration, quantify finis of risk carried, identify opportunities for diversification and balance the risk-return trade-off in their credit portfolio. The management of banks should strive to embrace the nonion of uncertainty and risk in their balance public opinion poll and instill the need for approaching credit establishment from a risk-perspective across the system by placing well drafted st estimategies in the detention of the ope rank staff with repayable material support for its succ essful implementation. on that point is a need for Strategic approach to assurance Risk direction (CRM) in IndianCommercial Banks, pointly in view of(1) gamyer(prenominal) NPAs level in comparison with global benchmark(2) RBI s stipulation intimately dividend dispersion by the banks(3) Revised NPAs level and gondola norms(4) New Basel groovy Accord (Basel -II) alteration1.2 OBJECTIVES To understand the conceptual framework for credit risk. To understand credit risk under the Basel II Accord. To test the credit risk management practices in a Leading Nationalised Bank1.3 RESEARCH METHODOLOGYResearch Design In order to have more comprehensive definition of the problem and to render familiar with the problems, an immense literature survey was d peerless to collect secondary selective culture for the fixing of the various shiftings, probably contemporary issues and the clarity of concepts.Data Collection Techniques The info collection technique employ is interviewing. D ata has been collected from both prototypalhand and secondary sources.Primary Data is collected by making own(prenominal) visits to the bank.Secondary Data The details have been collected from research papers, running(a)(a) papers, w concerne papers published by various agencies like ICRA, FICCI, IBA etc articles from the profit and various journals.1.4 LITERATURE REVIEW* Merton (1974) has use options de limitinal figureinationine amazeing as a technology to evaluate the credit risk of enterprise, it has been drawn a lot of attention from western academic and business circles.Mertons modelling is the supposedal foundation of structural models. Mertons model is not scarce ground on a strict and comprehensive theory but excessively utilise market knowledge stock determine as an signifi piece of tailt loss toevaluate the credit risk.This makes credit risk to be a real-time monitored at a much higher frequency.This advantage has made it widely use by the academic an d business circle for a long time. otherwise Structural Models try to refine the authoritative Merton Framework by removing angiotensin-converting enzyme or more of unrealistic assumptions.* B escape and Cox (1976) postulate that slights pass away as soon as firms summation tax falls on a unhorse floor a certain thresh aged. In contrast to the Merton approach, indifference can croak at any time. The paper by Black and Cox (1976) is the commencement exercise of the alleged(prenominal) First Passage Models (FPM). First passage models specify slackness as the commencement ceremony time the firms asset re re repute hits a dismount hindrance, allowing indifference to take beam at any time. When the scorn prohibition is exogenously fixed, as in Black and Cox (1976) and Longstaff and Schwartz (1995), it acts as a base hit covenant to protect obligeholders. Black and Cox introduce the possibility of more complex crownwork structures, with subordinated debt.* Geske (197 7) introduces beguile-paying debt to the Merton model.* Vasicek (1984) introduces the distinction between short and long barrier liabilities which now represents a distinctive feature of the KMV model.Under these models, all the germane(predicate) credit risk elements, including omission and re natural covering fireingy at neglect, atomic military issue 18 a function of the structural attributes of the firm asset levels, asset excitability (business risk) and supplement (financial risk).* Kim, Ramaswamy and Sund arsan (1993) have suggested an alternative approach which still adopts the legitimate Merton framework as far as the nonremittal process is concerned but, at the kindred(p) time, removes virtuoso of the unrealistic assumptions of the Merton model namely, that inadvertence can progress unless at adulthood of the debt when the firms assets atomic emergence 18 no longer competent to cover debt obligations. Instead, it is assumed that disrespect on may el iminate anytime between the issuance and maturity of the debt and that default is triggered when the determine of the firms assets reaches a lower threshold level. In this model, the RR in the event of default is exogenous and independent from the firms asset value. It is generally defined as a fixed ratio of the outstanding debt value and is on that pointfore independent from the PD.The attempt to overcome the shortcomings of structural-form models gave rise to reduced-form models. Unlike structural-form models, reduced-form models do not condition default on the value of the firm, and parameters think to the firms value need not be estimated to implement them.* Jarrow and Turnbull (1995) assumed that, at default, a seize would have a market value come to to an exogenously specified divide of an otherwise equivalent default-free bond.* Duffie and Singleton (1999) followed with a model that, when market value at default (i.e. RR) is exogenously specified, allows for closed-f orm solutions for the term-structure of credit spreads.* Zhou (2001) attempt to combine the advantages of structural-form models a choke economic mechanism behind the default process, and the virtuosos of reduced-form models unpredictability of default. This model links RRs to the firm value at default so that the chromosomal mutation in RRs is endogenously generated and the correlation between RRs and credit hunt downs reported runner in Altman (1989) and Gupton, Gates and Carty (2000) is justified.Lately portfolio view on credit losses has emerged by recognising that changes in credit quality fly the coop to comove over the business motor rack and that hotshot can diversify part of the credit risk by a clever composition of the loan portfolio across regions, industries and countries. Thus in order to assess the credit risk of a loan portfolio, a bank must(prenominal) not only investigate the credi iirthiness of its customers, but withal identify the concentration ri sks and likely comovements of risk factors in the portfolio.* CreditMetrics by Gupton et al (1997) was publicized in 1997 by JP Morgan. Its methodology is establish on probability of moving from nonpargonil credit quality to other(prenominal)(prenominal) within a given time horizon (credit migration synopsis). The estimation of the portfolio Value-at-Risk out-of-pocket to Credit (Credit-VaR) through CreditMetrics A evaluate system with probabilities of migrating from atomic derive 53 credit quality to some other over a given time horizon (transition matrix) is the key part of the credit-VaR proposed by JP Morgan. The specified credit risk horizon is usually one year. A rating system with probabilities of migrating from one credit quality to another over a given time horizon (transition matrix) is the key piece of the credit-VaR proposed by JP Morgan. The specified credit risk horizon is usually one year.* (Sy, 2007), states that the primary cause of credit default is loa n delinquency collect to in competent liquidity or money flow to service debt obligations. In the fountain of unsecured loans, we assume delinquency is a necessary and ample condition. In the human face of collateralized loans, delinquency is a necessary, but not commensurate condition, because the borrower may be able to re pay the loan from positive right or net assets to prevent default. In general, for secured loans, both delinquency and insolvency are assumed necessary and sufficient for credit default.CHAPTER 2THEORECTICAL FRAMEWORK2.1 credit rating adventureCredit risk is risk payable to uncertainty in a counterpartys (also called an obligors or credits) ability to meet its obligations. Because there are some types of counterpartiesfrom individuals to sovereign governmentsand many diverse types of obligationsfrom auto loans to derivatives exercisescredit risk takes many forms. Institutions manage it in contrary ways.Although credit losses by nature fluctuate o ver time and with economic conditions, there is (ceteris paribus) a statistically criteriond, long-run average loss level. The losses can be shared into devil categories i.e. expected losses (EL) and unexpected losses (UL).EL is ground on three parameters The likelihood that default will take commit over a specified time horizon (probability of default or PD) The amount owned by the counterparty at the moment of default (exposure at default or EAD) The work out of the exposure, net of any recoveries, which will be lost hobby a default event (loss given default or LGD).EL = PD x EAD x LGDEL can be aggregated at various various levels (e.g. individual loan or entire credit portfolio), although it is typically computed at the exploit level it is normally mentioned either as an absolute amount or as a percentage of transaction sizing. It is also both customer- and facility-specific, since two unlike loans to the same customer can have a actually different EL due to differenc es in EAD and/or LGD.It is grand to nib that EL (or, for that matter, credit quality) does not by itself constitute risk if losses always qualifieded their expected levels, then there would be no uncertainty. Instead, EL should be viewed as an anticipated cost of doing business and should therefore be inembodiedd in loan set and ex ante provisioning. Credit risk, in fact, arises from chance unsettleds in the actual loss levels, which give rise to the questionable unexpected loss (UL). Statistically speaking, UL is simply the standard passing of EL.UL= (EL) = (PD*EAD*LGD) at once the bank- level credit loss statistical scattering is constructed, credit economic cap is simply determined by the banks tolerance for credit risk, i.e. the bank needfully to decide how much outstanding it wants to hold in order to neutralize insolvency because of unexpected credit losses over the next year. A safer bank must have sufficient cap to withstand losses that are larger and rarer, i.e. they ex extend further out in the loss diffusion tail. In practice, therefore, the choice of confidence interval in the loss distribution corresponds to the banks target credit rating (and related default probability) for its own debt. As Figure to a lower place returns, economic gravid is the difference between EL and the selected confidence interval at the tail of the loss distribution it is equal to a multiple K ( genuinely much referred to as the not unfavourable(p) multiplier) of the standard deviation of EL (i.e. UL).The shape of the loss distribution can vary substantially depending on product type and borrower credit quality. For example, high quality (low PD) borrowers tend to have proportionally less EL per unit of majuscule charged, import that K is higher and the shape of their loss distribution is more reorient (and vice versa).Credit risk may be in the hobby forms * In example of the direct lending * In case of the guarantees and the earn of the cre dit * In case of the treasury operations * In case of the securities trading businesses * In case of the cross border exposure2.2 The need for Credit Risk paygradeThe need for Credit Risk range has arisen due to the following1. With dismantling of State control, deregulation, globalisation and allowing things to shape on the basis of market conditions, Indian Industry and Indian Banking face virgin risks and challenges. Competition results in the survival of the fittest. It is therefore necessary to identify these risks, measure them, monitor and control them.2. It provides a basis for Credit Risk equipment casualty i.e. fixation of rate of pertain on lending to different borrowers establish on their credit risk rating thereby balancing Risk Reward for the Bank.3. The Basel Accord and consequent Reserve Bank of India guidelines requires that the level of uppercase required to be maintained by the Bank will be in proportion to the risk of the loan in Banks Books for measuremen t of which square-toed Credit Risk Rating system is necessary.4. The credit risk rating can be a Risk Management tool for prospecting newly borrowers in addition to monitoring the weaker parameters and victorious remedial action.The types of Risks Captured in the Banks Credit Risk Rating ModelThe Credit Risk Rating Model provides a framework to evaluate the risk emanating from following main risk categorizes/risk areas* Industry risk * Business risk * Financial risk * Management risk * Facility risk * Project risk2.3 wherefore ascribe RISK MEASUREMENT?In recent years, a renewing is brewing in risk as it is both managed and measured. thither are seven reasons as to why certain surge in have-to doe with1. Structural increase in bankruptciesAlthough the most recent recession hit at different time in different countries, most statistics show a significant increase in bankruptcies, compared to prior recession. To the extent that there has been a permanent or structural increase in bankruptcies piecewide- due to increase in the global competition- accurate credit abbreviation run low even more important today than in past.2. DisintermediationAs capital markets have expanded and become penetrationible to small and mid surface firms, the firms or borrowers left behind to raise funds from banks and other traditional financial institutions (FIs) are likely to be smaller and to have weaker credit ratings. expectant market growth has bringd a winners curse effect on the portfolios of traditional FIs.3. More Competitive MarginsAlmost paradoxically, despite the decline in the average quality of loans, kindle margins or spreads, especially in in large quantities loan markets have become actually thin. In short, the risk-return trade off from lending has gotten worse. A government issue of reasons can be cited, but an important factor has been the enhanced competition for low quality borrowers especially from finance companies, much of whose lending activity has been concentrated at the higher risk/lower quality end of the market.4. Declining and Volatile Values of Collateral coincident with the recent Asian and Russian debt crisis in well developed countries much(prenominal) as Switzerland and Japan have shown that meetty and real assets value are very hard to predict, and to realize through liquidation. The weaker (and more uncertain) collateral determine are, the riskier the lending is likely to be. and so the current concerns about deflation worldwide have been accentuated the concerns about the value of real assets such(prenominal) as property and other physical assets.5. The Growth Of Off- Balance Sheet DerivativesIn many of the very large U.S. banks, the notional value of the off-balance- tag exposure to instruments such as over-the-counter(prenominal)(prenominal) (OTC) swaps and forwards is more than 10 times the size of their loan books. thence the growth in credit risk off the balance sheet was one of the main reason s for the introduction, by the Bank for International Settlements (BIS), of risk ground capital requirements in 1993. Under the BIS system, the banks have to hold a capital requirement ground on the mark- to- market current determine of each OTC Derivative contract plus an add on for strength in store(predicate) exposure.6. TechnologyAdvances in computer systems and related advances in nurture technology have given banks and FIs the opportunity to test high federal agencyed modeling techniques. A survey conducted by International Swaps and Derivatives Association and the install of International Finance in 2000 found that survey participants (consisting of 25 technicalised banks from 10 countries, with varying size and specialties) employ commercial and internal databases to assess the credit risk on rated and unrated commercial, retail and mortgage loans.7. The BIS Risk-Based Capital Requirements in spite of the importance of above six reasons, probably the greatest ince ntive for banks to develop new credit risk models has been dissatisfaction with the BIS and central banks post-1992 imposition of capital requirements on loans. The current BIS approach has been described as a one size fits all policy, irrespective of the size of loan, its maturity, and most importantly, the credit quality of the adoption party. Much of the current interest in fine tuning credit risk measurement models has been fueled by the proposed BIS New Capital Accord (or so Called BIS II) which would more closely link capital charges to the credit risk exposure to retail, commercial, sovereign and interbank credits.Chapter- 3Credit Risk Approaches and price3.1 mention RISK MEASUREMENT APPROACHES1. attribute SCORING exemplificationSCredit Scoring Models use data on observed borrower typicals to write in code the probability of default or to sort borrowers into different default risk manakines. By selecting and combining different economic and financial borrower characte ristics, a bank manager may be able to numerically establish which factors are important in explaining default risk, evaluate the relative degree or importance of these factors, improve the pricing of default risk, be better able to screen out disobedient loan applicants and be in a better position to calculate any reserve needed to meet expected future loan losses.To employ credit win model in this manner, the manager must identify objective economic and financial measures of risk for any peculiar(a) club of borrower. For consumer debt, the objective characteristics in a credit -scoring model tycoon implicate income, assets, age occupation and berth. For corporate debt, financial ratios such as debt- comeliness ratio are usually key factors. After data are identified, a statistical technique quantifies or scores the default risk probability or default risk classification.Credit scoring models hold three broad types (1) linear probability models, (2) logit model and (3) lin ear discriminant model.analogue PROBABILITY workThe linear probability model uses past data, such as accounting ratios, as foreplays into a model to explain refund experience on old loans. The relative importance of the factors employ in explaining the past repayment performance then forecasts repayment probabilities on new loans that is can be used for assessing the probability of repayment.Briefly we divide old loans (i) into two observational groups those that defaulted (Zi = 1) and those that did not default (Zi = 0). Then we relate these observations by linear regression to s set of j casual variables (Xij) that reflects quantative knowledge about the ith borrower, such as leverage or earnings. We estimate the model by linear regression ofZi = jXij + errorWhere j is the estimated importance of the jth variable in explaining past repayment experience. If we then take these estimated js and multiply them by the observed Xij for a prospective borrower, we can derive an expect ed value of Zi for the probability of repayment on the loan.LOGIT MODELThe objective of the typical credit or loan review model is to replicate judgments made by loan officers, credit managers or bank examiners. If an accurate model could be developed, then it could be used as a tool for reviewing and classifying future credit risks. Chesser (1974) developed a model to predict noncompliance with the customers original loan ar settingment, where non-compliance is defined to include not only default but any workout that may have been arranged resulting in a settlement of the loan less favorable to the tender than the original agreement.Chessers model, which was ground on a technique called logit abbreviation, consisted of the following six variables.X1 = (Cash + Marketable Securities)/ make sense AssetsX2 = Net Sales/(Cash + Marketable Securities)X3 = EBIT/ join AssetsX4 = supply Debt/ come up AssetsX5 = Total Assets/ Net WorthX6 = Working Capital/Net SalesThe estimated coefficien ts, including an knock term, areY = -2.0434 -5.24X1 + 0.0053X2 6.6507X3 + 4.4009X4 0.0791X5 0.1020X6Chessers classification rule for above equation is If P 50, frame to the non compliance group and If P50, fix to the compliance group. elongate DISCRIMINANT MODELWhile linear probability and logit models project a value opponent the expected probability of default if a loan is made, discriminant models divide borrowers into high or default risk classes contingent on their observed characteristic (X).Altmans Z-score model is an application of multivariate Discriminant analysis in credit risk modeling. Financial ratios measuring probability, liquidity and solvency appeared to have significant discriminating power to separate the firm that fails to service its debt from the firms that do not. These ratios are weighted to unwrap a measure (credit risk score) that can be used as a metric to differentiate the bad firms from the set of good ones.Discriminant analysis is a multivariat e statistical technique that essays a set of variables in order to differentiate two or more groups by minimizing the within-group division and maximizing the between group strain simultaneously. Variables taken wereX1Working Capital/ Total AssetX2 Retained Earning/ Total AssetX3 Earning before interest and taxes/ Total AssetX4 Market value of equity/ Book value of total LiabilitiesX5 Sales/Total AssetThe original Z-score model was rewrite and modified several times in order to find the scoring model more specific to a particular class of firm. These resulted in the private firms Z-score model, non manufacturers Z-score model and Emerging Market Scoring (EMS) model.3.2 New Approaches stipulation STRUCTURE DERIVATION OF confidence RISKOne market base method of assessing credit risk exposure and default probabilities is to analyze the risk premium inherent in the current structure of yields on corporate debt or loans to similar risk-rated borrowers. Rating agencies categorize co rporate bond issuers into at least seven major classes according to perceived credit quality. The foremost four ratings AAA, AA, A and BBB indicate investment quality borrowers.MORTALITY dictate APPROACHRather than extracting expected default rates from the current term structure of interest rates, the FI manager may analyze the historic or past default experience the deathrate rates, of bonds and loans of a similar quality. present p1is the probability of a grade B bond surviving the first year of its issue thus 1 p1 is the marginal fatality rate rate, or the probability of the bond or loan dying or defaulting in the first year while p2 is the probability of the loan surviving in the second year and that it has not defaulted in the first year, 1-p2 is the marginal mortality rate for the second year. Thus, for each grade of corporate buyer quality, a marginal mortality rate (MMR) curve can show the historic default rate in any specific quality class in each year after issue. RAROC MODELSBased on a banks risk-bearing capacity and its risk strategy, it is thus necessary bearing in beware the banks strategic orientation to find a method for the efficient take overation of capital to the banks individual siness areas, i.e. to define indicators that are suitable for balancing risk and return in a sensible manner. Indicators fulfilling this requirement are ofttimes referred to as risk adjusted performance measures (RAPM).RARORAC (risk adjusted return on risk adjusted capital, usually abbreviated as the most usually found forms are RORAC (return on risk adjusted capital),Net income is taken to mean income minus refinancing cost, operating cost, and expected losses. It should now be the banks tendency to maximize a RAPM indicator for the bank as a whole, e.g. RORAC, winning into account the correlation between individual transactions. Certain constraints such as volume restrictions due to a effectiveness lack of liquidity and the nourishment of solve ncy ground on economic and regulatory capital have to be observed in reaching this end. From an organizational point of view, value and risk management should therefore be linked as closely as accomplishable at all organizational levels.OPTION MODELS OF DEFAULT RISK (kmv model)KMV Corporation has developed a credit risk model that uses cultivation on the stock prices and the capital structure of the firm to estimate its default probability. The starting point of the model is the proposition that a firm will default only if its asset value falls below a certain level, which is function of its liability. It estimates the asset value of the firm and its asset volatility from the market value of equity and the debt structure in the option theoretic framework. The resultant probability is called Expected default Frequency (EDF). In summary, EDF is cypher in the following three whole tonesi) Estimation of asset value and volatility from the equity value and volatility of equity retu rn.ii) Calculation of hold from defaultiii) Calculation of expected default frequencyCredit inflectionIt provides a method for estimating the distribution of the value of the assets n a portfolio overpower to change in the credit quality of individual borrower. A portfolio consists of different stand-alone assets, defined by a stream of future notes flows. Each asset has a distribution over the possible range of future rating class. Starting from its initial rating, an asset may end up in ay one of the possible rating categories. Each rating category has a different credit spread, which will be used to discount the future cash flows. Moreover, the assets are correlated among themselves depending on the exertion they belong to. It is assumed that the asset returns are normally distributed and change in the asset returns causes the change in the rating category in future. Finally, the pretense technique is used to estimate the value distribution of the assets. A lean of scenari o are generated from a multivariate normal distribution, which is defined by the withdraw credit spread, the future value of asset is estimated.CREDIT Risk+CreditRisk+, introduced by Credit Suisse Financial Products (CSFP), is a model of default risk. Each asset has only two possible end-of-period states default and non-default. In the event of default, the loaner recovers a fixed proportion of the total expense. The default rate is considered as a continuous random variable. It does not try to estimate default correlation directly. hither, the default correlation is assumed to be determined by a set of risk factors. Conditional on these risk factors, default of each obligator follows a Bernoulli distribution. To get unconditional probability generating function for the number of defaults, it assumes that the risk factors are independently gamma distributed random variables. The final step in Creditrisk+ is to obtain the probability generating function for losses. Conditional on t he number of default events, the losses are entirely determined by the exposure and recovery rate. Thus, the distribution of asset can be estimated from the following gossip datai) Exposure of individual assetii) Expected default rateiii) Default ate volatilitiesiv) Recovery rate given default3.3 CREDIT PRICINGPricing of the credit is essential for the survival of enterprises relying on credit assets, because the benefits derived from extending credit should surpass the cost.With the introduction of capital adequacy norms, the credit risk is linked to the capital-minimum 8% capital adequacy. Consequently, higher capital is required to be deployed if more credit risks are underwritten. The decision (a) whether to maximize the returns on possible credit assets with the existing capital or (b) raise more capital to do more business invariably depends upon pCredit Risk DissertationCredit Risk DissertationCREDIT RISK administrator SUMMARYThe future of banking will undoubtedly rest on ri sk management dynamics. Only those banks that have efficient risk management system will survive in the market in the long run. The major cause of serious banking problems over the years continues to be directly related to lax credit standards for borrowers and counterparties, poor portfolio risk management, or a lack of attention to deterioration in the credit standing of a banks counterparties.Credit risk is the oldest and biggest risk that bank, by virtue of its very nature of business, inherits. This has however, acquired a greater significance in the recent past for various reasons. on that point have been many traditional approaches to measure credit risk like logit, linear probability model but with passage of time new approaches have been developed like the Credit+, KMV Model.Basel I Accord was introduced in 1988 to have a framework for regulatory capital for banks but the one size fit all approach led to a shift, to a new and comprehensive approach -Basel II which adopts a three anchor approach to risk management. Banks use a number of techniques to mitigate the credit risks to which they are exposed. RBI has prescribed adoption of comprehensive approach for the intent of CRM which allows fuller offset of security of collateral against exposures by effectively simplification the exposure amount by the value ascribed to the collateral.In this study, a in the lead nationalized bank is taken to study the steps taken by the bank to implement the Basel- II Accord and the entire framework developed for credit risk management. The bank under the study uses the credit scoring method to evaluate the credit risk involved in various loans/advances. The bank has set up special software to evaluate each case under various parameters and a monitoring system to continuously race each assets performance in accordance with the evaluation parameters.CHAPTER 1 grounding1.1 RationaleCredit Risk Management in todays deregulated market is a big challenge. Increased m arket volatility has brought with it the need for smart analysis and specialized applications in managing credit risk. A well defined policy framework is needed to help the operating staff identify the risk-event, assign a probability to each, quantify the likely loss, assess the acceptability of the exposure, price the risk and monitor them right to the point where they are paid off.Generally, Banks in India evaluate a proposal through the traditional tools of project financing, computer science maximum permissible limits, assessing management capabilities and prescribing a ceiling for an industry exposure. As banks move in to a new high cater world of financial operations and trading, with new risks, the need is felt for more ripe and versatile instruments for risk assessment, monitoring and controlling risk exposures. It is, therefore, time that banks managements check them fully to grapple with the demands of creating tools and systems capable of assessing, monitoring and co ntrolling risk exposures in a more scientific manner.According to an estimate, Credit Risk takes about 70% and 30% remaining is shared between the other two primary risks, namely Market risk (change in the market price and operational risk i.e., failure of internal controls, etc.). Quality borrowers (Tier-I borrowers) were able to access the capital market directly without going through the debt route. Hence, the credit route is now more open to lesser mortals (Tier-II borrowers). With margin levels going down, banks are unable to absorb the level of loan losses. Even in banks which regularly fine-tune credit policies and streamline credit processes, it is a real challenge for credit risk managers to correctly identify pockets of risk concentration, quantify extent of risk carried, identify opportunities for diversification and balance the risk-return trade-off in their credit portfolio. The management of banks should strive to embrace the notion of uncertainty and risk in their ba lance sheet and instill the need for approaching credit judgeship from a risk-perspective across the system by placing well drafted strategies in the workforce of the operating staff with due material support for its successful implementation.There is a need for Strategic approach to Credit Risk Management (CRM) in IndianCommercial Banks, particularly in view of(1) higher(prenominal) NPAs level in comparison with global benchmark(2) RBI s stipulation about dividend distribution by the banks(3) Revised NPAs level and railroad car norms(4) New Basel Capital Accord (Basel -II) revolution1.2 OBJECTIVES To understand the conceptual framework for credit risk. To understand credit risk under the Basel II Accord. To analyze the credit risk management practices in a Leading Nationalised Bank1.3 RESEARCH METHODOLOGYResearch Design In order to have more comprehensive definition of the problem and to become familiar with the problems, an extensive literature survey was done to collect second ary data for the location of the various variables, probably contemporary issues and the clarity of concepts.Data Collection Techniques The data collection technique used is interviewing. Data has been collected from both primary and secondary sources.Primary Data is collected by making person-to-person visits to the bank.Secondary Data The details have been collected from research papers, working papers, white papers published by various agencies like ICRA, FICCI, IBA etc articles from the net income and various journals.1.4 LITERATURE REVIEW* Merton (1974) has applied options pricing model as a technology to evaluate the credit risk of enterprise, it has been drawn a lot of attention from western academic and business circles.Mertons Model is the theoretical foundation of structural models. Mertons model is not only base on a strict and comprehensive theory but also used market knowledge stock price as an important version toevaluate the credit risk.This makes credit risk to be a real-time monitored at a much higher frequency.This advantage has made it widely applied by the academic and business circle for a long time. another(prenominal) Structural Models try to refine the original Merton Framework by removing one or more of unrealistic assumptions.* Black and Cox (1976) postulate that defaults occur as soon as firms asset value falls below a certain threshold. In contrast to the Merton approach, default can occur at any time. The paper by Black and Cox (1976) is the first of the so-called First Passage Models (FPM). First passage models specify default as the first time the firms asset value hits a lower barrier, allowing default to take place at any time. When the default barrier is exogenously fixed, as in Black and Cox (1976) and Longstaff and Schwartz (1995), it acts as a base hit covenant to protect bondholders. Black and Cox introduce the possibility of more complex capital structures, with subordinated debt.* Geske (1977) introduces interest-p aying debt to the Merton model.* Vasicek (1984) introduces the distinction between short and long term liabilities which now represents a distinctive feature of the KMV model.Under these models, all the applicable credit risk elements, including default and recovery at default, are a function of the structural characteristics of the firm asset levels, asset volatility (business risk) and leverage (financial risk).* Kim, Ramaswamy and Sundaresan (1993) have suggested an alternative approach which still adopts the original Merton framework as far as the default process is concerned but, at the same time, removes one of the unrealistic assumptions of the Merton model namely, that default can occur only at maturity of the debt when the firms assets are no longer sufficient to cover debt obligations. Instead, it is assumed that default may occur anytime between the issuance and maturity of the debt and that default is triggered when the value of the firms assets reaches a lower threshol d level. In this model, the RR in the event of default is exogenous and independent from the firms asset value. It is generally defined as a fixed ratio of the outstanding debt value and is therefore independent from the PD.The attempt to overcome the shortcomings of structural-form models gave rise to reduced-form models. Unlike structural-form models, reduced-form models do not condition default on the value of the firm, and parameters related to the firms value need not be estimated to implement them.* Jarrow and Turnbull (1995) assumed that, at default, a bond would have a market value equal to an exogenously specified fraction of an otherwise equivalent default-free bond.* Duffie and Singleton (1999) followed with a model that, when market value at default (i.e. RR) is exogenously specified, allows for closed-form solutions for the term-structure of credit spreads.* Zhou (2001) attempt to combine the advantages of structural-form models a clear economic mechanism behind the de fault process, and the ones of reduced-form models unpredictability of default. This model links RRs to the firm value at default so that the variation in RRs is endogenously generated and the correlation between RRs and credit ratings reported first in Altman (1989) and Gupton, Gates and Carty (2000) is justified.Lately portfolio view on credit losses has emerged by recognising that changes in credit quality tend to comove over the business cycle and that one can diversify part of the credit risk by a clever composition of the loan portfolio across regions, industries and countries. Thus in order to assess the credit risk of a loan portfolio, a bank must not only investigate the creditworthiness of its customers, but also identify the concentration risks and possible comovements of risk factors in the portfolio.* CreditMetrics by Gupton et al (1997) was publicized in 1997 by JP Morgan. Its methodology is based on probability of moving from one credit quality to another within a gi ven time horizon (credit migration analysis). The estimation of the portfolio Value-at-Risk due to Credit (Credit-VaR) through CreditMetrics A rating system with probabilities of migrating from one credit quality to another over a given time horizon (transition matrix) is the key factor of the credit-VaR proposed by JP Morgan. The specified credit risk horizon is usually one year. A rating system with probabilities of migrating from one credit quality to another over a given time horizon (transition matrix) is the key share of the credit-VaR proposed by JP Morgan. The specified credit risk horizon is usually one year.* (Sy, 2007), states that the primary cause of credit default is loan delinquency due to insufficient liquidity or cash flow to service debt obligations. In the case of unsecured loans, we assume delinquency is a necessary and sufficient condition. In the case of collateralized loans, delinquency is a necessary, but not sufficient condition, because the borrower may b e able to refinance the loan from positive equity or net assets to prevent default. In general, for secured loans, both delinquency and insolvency are assumed necessary and sufficient for credit default.CHAPTER 2THEORECTICAL FRAMEWORK2.1 CREDIT RISKCredit risk is risk due to uncertainty in a counterpartys (also called an obligors or credits) ability to meet its obligations. Because there are many types of counterpartiesfrom individuals to sovereign governmentsand many different types of obligationsfrom auto loans to derivatives transactionscredit risk takes many forms. Institutions manage it in different ways.Although credit losses by nature fluctuate over time and with economic conditions, there is (ceteris paribus) a statistically measured, long-run average loss level. The losses can be change integrity into two categories i.e. expected losses (EL) and unexpected losses (UL).EL is based on three parameters The likelihood that default will take place over a specified time horizon (probability of default or PD) The amount owned by the counterparty at the moment of default (exposure at default or EAD) The fraction of the exposure, net of any recoveries, which will be lost following a default event (loss given default or LGD).EL = PD x EAD x LGDEL can be aggregated at various different levels (e.g. individual loan or entire credit portfolio), although it is typically work out at the transaction level it is normally mentioned either as an absolute amount or as a percentage of transaction size. It is also both customer- and facility-specific, since two different loans to the same customer can have a very different EL due to differences in EAD and/or LGD.It is important to wrinkle that EL (or, for that matter, credit quality) does not by itself constitute risk if losses always equaled their expected levels, then there would be no uncertainty. Instead, EL should be viewed as an anticipated cost of doing business and should therefore be incorporated in loan prici ng and ex ante provisioning. Credit risk, in fact, arises from variations in the actual loss levels, which give rise to the so-called unexpected loss (UL). Statistically speaking, UL is simply the standard deviation of EL.UL= (EL) = (PD*EAD*LGD) erst the bank- level credit loss distribution is constructed, credit economic capital is simply determined by the banks tolerance for credit risk, i.e. the bank needfully to decide how much capital it wants to hold in order to vacate insolvency because of unexpected credit losses over the next year. A safer bank must have sufficient capital to withstand losses that are larger and rarer, i.e. they extend further out in the loss distribution tail. In practice, therefore, the choice of confidence interval in the loss distribution corresponds to the banks target credit rating (and related default probability) for its own debt. As Figure below shows, economic capital is the difference between EL and the selected confidence interval at the tai l of the loss distribution it is equal to a multiple K (often referred to as the capital multiplier) of the standard deviation of EL (i.e. UL).The shape of the loss distribution can vary considerably depending on product type and borrower credit quality. For example, high quality (low PD) borrowers tend to have proportionally less EL per unit of capital charged, message that K is higher and the shape of their loss distribution is more reorient (and vice versa).Credit risk may be in the following forms * In case of the direct lending * In case of the guarantees and the letter of the credit * In case of the treasury operations * In case of the securities trading businesses * In case of the cross border exposure2.2 The need for Credit Risk RatingThe need for Credit Risk Rating has arisen due to the following1. With dismantling of State control, deregulation, globalisation and allowing things to shape on the basis of market conditions, Indian Industry and Indian Banking face new risks and challenges. Competition results in the survival of the fittest. It is therefore necessary to identify these risks, measure them, monitor and control them.2. It provides a basis for Credit Risk Pricing i.e. fixation of rate of interest on lending to different borrowers based on their credit risk rating thereby balancing Risk Reward for the Bank.3. The Basel Accord and consequent Reserve Bank of India guidelines requires that the level of capital required to be maintained by the Bank will be in proportion to the risk of the loan in Banks Books for measurement of which proper Credit Risk Rating system is necessary.4. The credit risk rating can be a Risk Management tool for prospecting rattling borrowers in addition to monitoring the weaker parameters and taking remedial action.The types of Risks Captured in the Banks Credit Risk Rating ModelThe Credit Risk Rating Model provides a framework to evaluate the risk emanating from following main risk categorizes/risk areas* Industry r isk * Business risk * Financial risk * Management risk * Facility risk * Project risk2.3 wherefore CREDIT RISK MEASUREMENT?In recent years, a revolution is brewing in risk as it is both managed and measured. There are seven reasons as to why certain surge in interest1. Structural increase in bankruptciesAlthough the most recent recession hit at different time in different countries, most statistics show a significant increase in bankruptcies, compared to prior recession. To the extent that there has been a permanent or structural increase in bankruptcies worldwide- due to increase in the global competition- accurate credit analysis become even more important today than in past.2. DisintermediationAs capital markets have expanded and become accessible to small and mid coat firms, the firms or borrowers left behind to raise funds from banks and other traditional financial institutions (FIs) are likely to be smaller and to have weaker credit ratings. Capital market growth has produce d a winners curse effect on the portfolios of traditional FIs.3. More Competitive MarginsAlmost paradoxically, despite the decline in the average quality of loans, interest margins or spreads, especially in wholesale loan markets have become very thin. In short, the risk-return trade off from lending has gotten worse. A number of reasons can be cited, but an important factor has been the enhanced competition for low quality borrowers especially from finance companies, much of whose lending activity has been concentrated at the higher risk/lower quality end of the market.4. Declining and Volatile Values of Collateral cooccurring with the recent Asian and Russian debt crisis in well developed countries such as Switzerland and Japan have shown that property and real assets value are very hard to predict, and to realize through liquidation. The weaker (and more uncertain) collateral values are, the riskier the lending is likely to be. Indeed the current concerns about deflation worldwi de have been accentuated the concerns about the value of real assets such as property and other physical assets.5. The Growth Of Off- Balance Sheet DerivativesIn many of the very large U.S. banks, the notional value of the off-balance-sheet exposure to instruments such as over-the-counter (OTC) swaps and forwards is more than 10 times the size of their loan books. Indeed the growth in credit risk off the balance sheet was one of the main reasons for the introduction, by the Bank for International Settlements (BIS), of risk based capital requirements in 1993. Under the BIS system, the banks have to hold a capital requirement based on the mark- to- market current values of each OTC Derivative contract plus an add on for potential future exposure.6. TechnologyAdvances in computer systems and related advances in information technology have given banks and FIs the opportunity to test high powered modeling techniques. A survey conducted by International Swaps and Derivatives Association a nd the land of International Finance in 2000 found that survey participants (consisting of 25 commercial banks from 10 countries, with varying size and specialties) used commercial and internal databases to assess the credit risk on rated and unrated commercial, retail and mortgage loans.7. The BIS Risk-Based Capital Requirements in spite of the importance of above six reasons, probably the greatest incentive for banks to develop new credit risk models has been dissatisfaction with the BIS and central banks post-1992 imposition of capital requirements on loans. The current BIS approach has been described as a one size fits all policy, irrespective of the size of loan, its maturity, and most importantly, the credit quality of the acquire party. Much of the current interest in fine tuning credit risk measurement models has been fueled by the proposed BIS New Capital Accord (or so Called BIS II) which would more closely link capital charges to the credit risk exposure to retail, comm ercial, sovereign and interbank credits.Chapter- 3Credit Risk Approaches and Pricing3.1 CREDIT RISK MEASUREMENT APPROACHES1. CREDIT SCORING MODELSCredit Scoring Models use data on observed borrower characteristics to calculate the probability of default or to sort borrowers into different default risk classes. By selecting and combining different economic and financial borrower characteristics, a bank manager may be able to numerically establish which factors are important in explaining default risk, evaluate the relative degree or importance of these factors, improve the pricing of default risk, be better able to screen out bad loan applicants and be in a better position to calculate any reserve needed to meet expected future loan losses.To employ credit scoring model in this manner, the manager must identify objective economic and financial measures of risk for any particular class of borrower. For consumer debt, the objective characteristics in a credit -scoring model talent inc lude income, assets, age occupation and location. For corporate debt, financial ratios such as debt-equity ratio are usually key factors. After data are identified, a statistical technique quantifies or scores the default risk probability or default risk classification.Credit scoring models include three broad types (1) linear probability models, (2) logit model and (3) linear discriminant model.LINEAR PROBABILITY MODELThe linear probability model uses past data, such as accounting ratios, as inputs into a model to explain repayment experience on old loans. The relative importance of the factors used in explaining the past repayment performance then forecasts repayment probabilities on new loans that is can be used for assessing the probability of repayment.Briefly we divide old loans (i) into two observational groups those that defaulted (Zi = 1) and those that did not default (Zi = 0). Then we relate these observations by linear regression to s set of j casual variables (Xij) that reflects quantative information about the ith borrower, such as leverage or earnings. We estimate the model by linear regression ofZi = jXij + errorWhere j is the estimated importance of the jth variable in explaining past repayment experience. If we then take these estimated js and multiply them by the observed Xij for a prospective borrower, we can derive an expected value of Zi for the probability of repayment on the loan.LOGIT MODELThe objective of the typical credit or loan review model is to replicate judgments made by loan officers, credit managers or bank examiners. If an accurate model could be developed, then it could be used as a tool for reviewing and classifying future credit risks. Chesser (1974) developed a model to predict noncompliance with the customers original loan arrangement, where non-compliance is defined to include not only default but any workout that may have been arranged resulting in a settlement of the loan less favorable to the tender than the origin al agreement.Chessers model, which was based on a technique called logit analysis, consisted of the following six variables.X1 = (Cash + Marketable Securities)/Total AssetsX2 = Net Sales/(Cash + Marketable Securities)X3 = EBIT/Total AssetsX4 = Total Debt/Total AssetsX5 = Total Assets/ Net WorthX6 = Working Capital/Net SalesThe estimated coefficients, including an hold back term, areY = -2.0434 -5.24X1 + 0.0053X2 6.6507X3 + 4.4009X4 0.0791X5 0.1020X6Chessers classification rule for above equation is If P 50, assign to the non compliance group and If P50, assign to the compliance group.LINEAR DISCRIMINANT MODELWhile linear probability and logit models project a value confrontation the expected probability of default if a loan is made, discriminant models divide borrowers into high or default risk classes contingent on their observed characteristic (X).Altmans Z-score model is an application of multivariate Discriminant analysis in credit risk modeling. Financial ratios measuring probability, liquidity and solvency appeared to have significant discriminating power to separate the firm that fails to service its debt from the firms that do not. These ratios are weighted to produce a measure (credit risk score) that can be used as a metric to differentiate the bad firms from the set of good ones.Discriminant analysis is a multivariate statistical technique that analyzes a set of variables in order to differentiate two or more groups by minimizing the within-group variance and maximizing the between group variance simultaneously. Variables taken wereX1Working Capital/ Total AssetX2 Retained Earning/ Total AssetX3 Earning before interest and taxes/ Total AssetX4 Market value of equity/ Book value of total LiabilitiesX5 Sales/Total AssetThe original Z-score model was revise and modified several times in order to find the scoring model more specific to a particular class of firm. These resulted in the private firms Z-score model, non manufacturers Z-score model and Emerging Market Scoring (EMS) model.3.2 New Approaches condition STRUCTURE DERIVATION OF CREDIT RISKOne market based method of assessing credit risk exposure and default probabilities is to analyze the risk premium inherent in the current structure of yields on corporate debt or loans to similar risk-rated borrowers. Rating agencies categorize corporate bond issuers into at least seven major classes according to perceived credit quality. The first four ratings AAA, AA, A and BBB indicate investment quality borrowers.MORTALITY cast APPROACHRather than extracting expected default rates from the current term structure of interest rates, the FI manager may analyze the historic or past default experience the mortality rates, of bonds and loans of a similar quality. Here p1is the probability of a grade B bond surviving the first year of its issue thus 1 p1 is the marginal mortality rate, or the probability of the bond or loan dying or defaulting in the first year while p2 is the prob ability of the loan surviving in the second year and that it has not defaulted in the first year, 1-p2 is the marginal mortality rate for the second year. Thus, for each grade of corporate buyer quality, a marginal mortality rate (MMR) curve can show the diachronic default rate in any specific quality class in each year after issue.RAROC MODELSBased on a banks risk-bearing capacity and its risk strategy, it is thus necessary bearing in fountainhead the banks strategic orientation to find a method for the efficient allotment of capital to the banks individual siness areas, i.e. to define indicators that are suitable for balancing risk and return in a sensible manner. Indicators fulfilling this requirement are often referred to as risk adjusted performance measures (RAPM).RARORAC (risk adjusted return on risk adjusted capital, usually abbreviated as the most commonly found forms are RORAC (return on risk adjusted capital),Net income is taken to mean income minus refinancing cost, operating cost, and expected losses. It should now be the banks goal to maximize a RAPM indicator for the bank as a whole, e.g. RORAC, taking into account the correlation between individual transactions. Certain constraints such as volume restrictions due to a potential lack of liquidity and the livelihood of solvency based on economic and regulatory capital have to be observed in reaching this goal. From an organizational point of view, value and risk management should therefore be linked as closely as possible at all organizational levels.OPTION MODELS OF DEFAULT RISK (kmv model)KMV Corporation has developed a credit risk model that uses information on the stock prices and the capital structure of the firm to estimate its default probability. The starting point of the model is the proposition that a firm will default only if its asset value falls below a certain level, which is function of its liability. It estimates the asset value of the firm and its asset volatility from the market value of equity and the debt structure in the option theoretic framework. The resultant probability is called Expected default Frequency (EDF). In summary, EDF is calculated in the following three stepsi) Estimation of asset value and volatility from the equity value and volatility of equity return.ii) Calculation of remoteness from defaultiii) Calculation of expected default frequencyCredit inflectionIt provides a method for estimating the distribution of the value of the assets n a portfolio battleground to change in the credit quality of individual borrower. A portfolio consists of different stand-alone assets, defined by a stream of future cash flows. Each asset has a distribution over the possible range of future rating class. Starting from its initial rating, an asset may end up in ay one of the possible rating categories. Each rating category has a different credit spread, which will be used to discount the future cash flows. Moreover, the assets are correlated amon g themselves depending on the industry they belong to. It is assumed that the asset returns are normally distributed and change in the asset returns causes the change in the rating category in future. Finally, the theoretical account technique is used to estimate the value distribution of the assets. A number of scenario are generated from a multivariate normal distribution, which is defined by the curb credit spread, the future value of asset is estimated.CREDIT Risk+CreditRisk+, introduced by Credit Suisse Financial Products (CSFP), is a model of default risk. Each asset has only two possible end-of-period states default and non-default. In the event of default, the loaner recovers a fixed proportion of the total expense. The default rate is considered as a continuous random variable. It does not try to estimate default correlation directly. Here, the default correlation is assumed to be determined by a set of risk factors. Conditional on these risk factors, default of each obl igator follows a Bernoulli distribution. To get unconditional probability generating function for the number of defaults, it assumes that the risk factors are independently gamma distributed random variables. The final step in Creditrisk+ is to obtain the probability generating function for losses. Conditional on the number of default events, the losses are entirely determined by the exposure and recovery rate. Thus, the distribution of asset can be estimated from the following input datai) Exposure of individual assetii) Expected default rateiii) Default ate volatilitiesiv) Recovery rate given default3.3 CREDIT PRICINGPricing of the credit is essential for the survival of enterprises relying on credit assets, because the benefits derived from extending credit should surpass the cost.With the introduction of capital adequacy norms, the credit risk is linked to the capital-minimum 8% capital adequacy. Consequently, higher capital is required to be deployed if more credit risks are un derwritten. The decision (a) whether to maximize the returns on possible credit assets with the existing capital or (b) raise more capital to do more business invariably depends upon p