GLOBALIZATION QUESTION Essay Example | Topics and Well Written Essays - 750 words

GLOBALIZATION QUESTION - Essay Example Religious practices derive meaning and force from integration with cultural beliefs and practices. Across different denominations, religion derives its truth value from its dependence and connection with the cultural fabric. Religious truth is based on the ability of the underlying tradition to provide its followers with a predefined worldview over a long period of time. Moreover, the world view should have meaning, Lead to physical and spiritual integration, moral guidance, hope and faith in both the present and the future. Religions like Christianity, zoroasticism, Buddhism, Judaism and Islam claim absolute validity but it is expressed through different structures (Mary, 17). From history, most religions were recognized nationally and thus force followers to recognize its teachings for example, Islam in Arab countries. Religious truth is contradicted by the relative interpretations adopted to define situations and preferably make them fit into the speaker’s worldview. For in stance, in the early Greek philosophy, Socrates used truth as claptrap for the public orators through the use of conventional notions (Kluckhohn 6). The opposition between nature and convection hinders man from speaking out his mind but rather to conform to what the society believes in. The confusion leads to lack of a clear cut definition of what is considered as conventional and natural truth. According to Conze (153), naturally, all shameful things are evil like injustice is shunned by men and only slaves are meant to suffer injustice. In Buddhism teachings, common sense and spiritual truth are considered to be the two distinct categories of truth that exist. According to the doctrine, both truths coexist and are the basis of the Budhi religion (Bodhi 20) . Through several assertions, Christianity professes the existence of doctrinal truth. For instance, Jesus Christ is considered to be the truth in the doctrine through his words â€Å"I am the way and the truth and the life, no one comes to the father but through me’ (Stephen 45). In Hinduism, truth is part of the ten religious attributes of Dharma. Believers are required to be truthful and to speak only of what they have seen and understood. For example, in India, â€Å"Rishi†, truth, entails existence, truth of ones being, and truth of being God (Kluckhohn 367). In Judaism, in the old testament, truth is the word of God and followers believe that spiritual leaders are truthful and have power to deliver divine intervention. Despite the difference in religious symbols from one culture to another, they serve the same purpose of helping the society deal with issues that are beyond human control. However, the system is based on simple truths as defined by different societies. For example, Christianity and Buddhism shun laziness and encourage independence, courage, endurance, and desire to live for the good of everyone. In Christianity, a man’s divinity in his feelings is more important t han concepts because the desire to live up to the concepts causes contradiction in his or her character (Goody 97). Religion creates consciousness in individuals in how he should relate with others and use the lessons to sail through life’s tribulations positively. Religions are part of culture in different societie

Sex in Advertising Research Paper Example | Topics and Well Written Essays - 1250 words - 1

Sex in Advertising - Research Paper Example The advertisement industry prefers to portray females as objects of enjoyment and appeal. It seems that females in the last two decades have become well-used to the needs of the advertising industry and have decided to explore the possibility. The advertisers have been reminding the society that female body is valuable, and something that must be revealed. All beauty products in the market are accompanied by almost fully naked female bodies. Famous brands and celebrities also contribute to this sex-oriented marketing system and get benefits in return. In order to draw consumer attention, the performers in ads dress provocatively, exhibit incongruously seductive, and behave in a flirtatious manner. A look into such ads reveals that the definition of beauty has changed from time to time. It is easy to understand this difference if one compares a beautiful woman of the present day with the portrait of a beautiful woman of the 16th century. Undoubtedly, the concept of fashion is set and redefined by the advertising media from time to time. According to Aaslestad, fashion embodies new social values and emerges as the primary area of contradiction between tradition and change (283). This is very evident in the changes that took place in the dressing of celebrities, namely women, appeared in ads. There is a visible change in the styles of both men and women today, and this change is attributed to the new philosophical and social ideals cherished by the advertising industry namely women, appeared in ads.

Analyse the organisational behaviour issues that contributed to the Assignment

Analyse the organisational behaviour issues that contributed to the leadership challenges at HP - Assignment Example HP began humbly at the back of its entrepreneur’s garage. Engineers David Packard and Bill Hewlett established an unconventional company in the 1950s. They wanted to build a business environment in which members were free to express their ideas and make contributions; they thus created a flat structure. Many individuals in Silicon Valley praised the organisation for its revolutionary ways. During the mid 1990s, employees had a profound respect for the institution. They carried on with their jobs despite the tumultuous environment that pervaded the IT industry. Several individuals felt loyal to the organisation even though the company had to subject them to pay cuts. Members trusted the company’s leaders as strong levels of communication existed in the organisation. These trends altered dramatically when the company decided to hire an external executive for the first time in the late 1990s. The move was initiated by the departing CEO who felt that HP had become complacen t. Some of its competitors in Silicon Valley were exemplars of innovation and growth. It was assumed that an external leader would inject the much-needed fresh insight into the company. However, such a move proved to be ill-conceived because it was the beginning of several tumultuous events in the organisation. At the beginning, CEO Fiorina seemed like a forward-thinker. She initiated various restructures within HP and even lobbied for the acquisition of a competitor - Compaq. The latter move would prove to be her downfall as it failed to yield the expected outcomes. Shortly after a period of disappointing results, Fiorina resigned and made room for Mark Hurd. He was a transformative leader who engaged with his employees. He also supervised a massive layoff and delayered the firm structures. However, he was involved in a personal scandal that led to his elimination. His replacement, Leo Apotheker, was a pale comparison to Hurd. He failed to improve stock performance and even discont inued winning strategies. The final straw fell when he bought a business analytics company called Autonomy. Stock prices decreased tumultuously thereby signalling his elimination. These leadership challenges were also amalgamated by disputes in the board. The paper will examine organisational behaviour issues that led to the firm’s woes. Analysis of the situation Leadership The situational leadership theory assumes that different situations require different leadership traits. Therefore advocates of the school of thought believe that no profile of leadership is perfect for all situations (Hope and Hendry, 1995). Instead, utmost attention should be given to the variables within a certain situation and the right approach selected for them. These variables include the nature of work tasks, peer expectations, other leaders’ expectations, the culture and climate of the organisation, and followers’ responses. In some instances, a leader may need to be a risk taker whi le in some scenarios; the person may have to exhibit nurturing traits (Buchanan and Boddy, 1992). Sometimes it may be preferable to be charismatic while in some situations it would be best to focus on results. Hewlett Packard may have benefited from applications of situational approaches to leadership during the three tenures under analysis. CEO Carl Fiorina appeared to apply the same leadership traits in divergent situations and this could have explained her dissatisfactory performance. She was highly transformative; as soon as she

Sex in Advertising Research Paper Example | Topics and Well Written Essays - 1250 words - 1

Sex in Advertising - Research Paper Example The advertisement industry prefers to portray females as objects of enjoyment and appeal. It seems that females in the last two decades have become well-used to the needs of the advertising industry and have decided to explore the possibility. The advertisers have been reminding the society that female body is valuable, and something that must be revealed. All beauty products in the market are accompanied by almost fully naked female bodies. Famous brands and celebrities also contribute to this sex-oriented marketing system and get benefits in return. In order to draw consumer attention, the performers in ads dress provocatively, exhibit incongruously seductive, and behave in a flirtatious manner. A look into such ads reveals that the definition of beauty has changed from time to time. It is easy to understand this difference if one compares a beautiful woman of the present day with the portrait of a beautiful woman of the 16th century. Undoubtedly, the concept of fashion is set and redefined by the advertising media from time to time. According to Aaslestad, fashion embodies new social values and emerges as the primary area of contradiction between tradition and change (283). This is very evident in the changes that took place in the dressing of celebrities, namely women, appeared in ads. There is a visible change in the styles of both men and women today, and this change is attributed to the new philosophical and social ideals cherished by the advertising industry namely women, appeared in ads.

Improving Your Writing Skills Essay Example | Topics and Well Written Essays - 750 words

Improving Your Writing Skills - Essay Example This study outlines that there are several issues that should be put into consideration. The writer should organize the information using a functional format. A functional format is an arrangement that goes through a sequence as per the material that is being presented for clarity and easy understanding. This should be followed by writing down of a draft that comprises of all the important parts of the information that is to be passed to the reader. The writing of a draft is an important process as it gives the writer the opportunity to exhaust all the information that ought to be included, make changes through by removing or adding part of the document. Thus, only the relevant information gets the chance of being in the draft. It is after this process, that the final document for the reader is prepared. In order to be a good business writer, good business writing skills are essential. Through this, professionalism is observed from either side. In this regard, a business document nee ds to be short and precise. This is because most people who require this information do not have much time to go through long documents in search of the ideas that have been put down by the writers.

The Necklace by Guy de Maupassant Essay Example for Free

The Necklace by Guy de Maupassant Essay I. iNTRODUCTION TO FRENCH LITERATURE French literature is, generally speaking, literature written in the French language, particularly by citizens of France; it may also refer to literature written by people living in France who speak traditional languages of France other than French. Literature written in French language, by citizens of other nations such as Belgium, Switzerland, Canada, Senegal, Algeria, Morocco, etc. is referred to as Francophone literature. As of 2006, French writers have been awarded more Nobel Prizes in Literature than novelists, poets and essayists of any other country. France itself ranks first in the list of Nobel Prizes in literature by country. The French language is a romance dialect derived from Vulgar Latin (non-standard Latin) and heavily influenced principally by Celtic and Frankish. Beginning in the 11th century, literature written in medieval French was one of the oldest vernacular (non-Latin) literatures in western Europe and it became a key source of literary themes in the Middle Ages across the continent. Although the European prominence of French literature was eclipsed in part by vernacular literature in Italy in the 14th century, literature in France in the 16th century underwent a major creative evolution, and through the political and artistic programs of the Ancien Rà ©gime, French literature came to dominate European letters in the 17th century. In the 18th century, French became the literary lingua franca and diplomatic language of western Europe (and, to a certain degree, in America), and French letters have had a profound impact on all European and American literary traditions while at the same time being heavily influenced by these other national traditions (for example: British and German Romanticism in the nineteenth century). French literary developments of the 19th and 20th centuries have had a particularly strong effect on modern world literature, including: symbolism, naturalism, the roman-fleuves of Balzac, Zola and Proust, surrealism, existentialism, and the Theatre of the Absurd. French imperialism and colonialism in the Americas, Africa, and the far East have brought the French language to non-European cultures that are transforming and adding to the French literary experience today. II. aUthor’s biography Guy de Maupassant Henri-Renà ©-Albert-Guy de Maupassant was born on August 5, 1850 at the chà ¢teau de Miromesnil, near Dieppe in the Seine-Infà ©rieure (now Seine-Maritime) department in France. He was the first son of Laure Le Poittevin and Gustave de Maupassant, both from prosperous bourgeois families. When Maupassant was 37 and his brother Hervà © was five, his mother, an independent-minded woman, risked social disgrace to obtain a legal separation from her husband. After the separation, Le Poittevin kept her two sons, the elder Guy and younger Hervà ©. With the father’s absence, Maupassant’s mother became the most influential figure in the young boy’s life. She was an exceptionally well read woman and was very fond of classical literature, especially Shakespeare. Until the age of thirteen, Guy happily lived with his mother, to whom he was deeply devoted, at Étretat, in the Villa des Verguies, where, between the sea and the luxuriant countryside, he grew very fond of fishing and outdoor activities. III. Elements of a Short Story III. Elements of a Short Story A. Setting of the Story * Time: 19th Century, Second Half * Place: Paris, France B. Characters: * Mathilde Loisel-a pretty young woman born into a common, middle-class family. She yearns for wealth, privileges, and fashions of highborn young ladies * Monsieur Loisel-a government clerk in the Ministry of Education whom Mathilde marries * Madame Jeanne Forestier-a friend of Mathilde’s. She allows Mathilde to borrow a necklace to wear to a gala social event. * Loisel Housemaid-a girl from Brittany who does the Loisel’s housework. Her presence reminds Mathilde of her own status as a commoner * C. Plot C. Plot Monsieur and Madame Georges Rampounneau-Minister of Education, and his wife. They invite the Loisels to the party. C1. Exposition Mathilde is a pretty and charming woman, born of simple roots and humble beginnings, relished with both the love and warmth of a family though not well-off financially yet considerably contemporary to the families in the middle of the hierarchy. She was married to Monsieur Loisel, a government clerk who works round-the-clock at the Ministry of Education. She has always dreamt of a life of luxury and leisure, with attentive maidservants, a large home decorated with coveted linens, expensive jewels and fancy silverware. Mortified of the humiliating state she’s in, she no longer visits Madame Forestier, an old friend of hers. C2. Rising Action The Loisels receive an envelope with a letter inviting them to an affair at the Ministry of Education, as honored guests of Monsieur Georges Rampouneau, Head and Minister to Education. Monsiuer Loisel gets an expression completely opposite to what he was expecting for. Mathilde grows worried and tirelessly distraught for she has not a single dress to wear for the occasion. She needs something extravagant and fancy, but a piece of clothing of such delicate formality would cost Monsieur Loisel a sum of four hundred Francs-the exact amount he’s been saving for to buy himself a rifle. The day of the fete draws nearer, and Mathilde becomes increasingly downcast and hopeless. Loisel begins to ask Mathilde the cause of her misery, and is later greeted with an answer of coveted jewelry. Monsieur Loisel suggests that she borrows jewels from her friend, Madame Jeanne Forestier. Mathilde wastes no time and visits her the following morning. Madame Forestier, agreeable and willing to coope rate, opens a box and tells her to choose one. Glittering jewels and sought-after handcrafted gems later, Mathilde cherry-picks a necklace, one encrusted with diamonds of genuine value. C3. Climax The day of the party comes and Mathilde becomes the center of everybody’s attention. Highly-acquainted men of noble stature all ask who she is and start to line-up to dance with her. The Loisels revel in joy and merriment and left no longer than four in the morning. On their way out, Monsiuer Loisel puts a wrap around Mathilde’s shoulders-a piece of clothing from her daily wardrobe. She hurries out hastily to prevent herself from being seen in it. Subject to the frigid coldness of the early morning, they look for means of transportation. They later find a cab and are took back home to the Rues de Martyrs. In her bedroom, Mathilde stands before the mirror and gazes intently at the woman who has beguiled so many men. Then out of sheer horror, she untimely realizes that the necklace is gone. Mathilde begins to search through their things while Monsiuer Loisel retraces their steps, hopeful that he might stumble across the necklace they’ve lost. With bitter hopes and foul resentment, they find nothing and return empty-handed. C4. Falling Action Mathilde decides to write to Madame Forestier, informing her that the necklace’s clasp has been broken and is being repaired. They conclude that their only recourse is to replace it all in due time. They traverse Paris and go from jeweler to jeweler, hoping to know how a necklace of such appraisal could cost them. The Loisels find one at the Palais Royal, with a staggering value of thirty-six thousand Francs. To raise enough money, Monsiuer Loisel spends all of his savings and decides to borrow the rest, writing promissory notes and placing signature after signature on numerous contracts. The Loisels manage to buy it, and Mathilde takes it to Madame Forestier, who is considerably aggravated at how late it was given. The couple, thereafter, struggles to pay their debt. Mathilde dismisses their housemaid and does the housework herself-washing dishes, taking out garbage, and fulfilling other lowly pains. Monsieur Loisel, on the other hand, shifts to a bookkeeper and copyist. C5. Denouement A decade later, they manage to free themselves from debt. By this time, Mathilde is a full-on unmistakable commoner. She staggers with rough hands, unornamented clothes, and disheveled hair. Occasionally, she reminisces back to the day when she still had the necklace and when so many men admired her. What, then, would have happened if she never lost the necklace in the first place? On one Sunday morning at the Champs Elysees, she encounters Madame Forestier. Mathilde addresses her yet Madame Forestier vaguely remembers anything at the spark of insight. After Mathilde identifies herself, she decides to tell her the truth. There would be no consequence or harm in fessing up since the necklace has already been paid full-on in Francs now-through all those painstaking nights of menial tasks and humble labors, working tirelessly to measure up to her obligation. But Mathilde never knew the other side of the story when she borrowed the necklace on that fateful day in France. It was fake, a non-discrete imitation with counterfeit diamonds and phony encrusted jewels. At most, it was worth five-hundred Francs, a sum evidently not worth wasting ten long years on staggering debt. C6. Theme * Appearances are Deceiving * Appearances are Deceiving Mathilde Loisel believed the necklace genuine the moment she saw it. Likewise, she believed that all the people at the party were real, genuine human beings because of their social standing and their possessions. The necklace, of course, was a fake. And, Maupassant implies, so were the people at the party who judged her on her outward appearance. v. creative presentation Appearances are Appearances are deceiving. not everything deceiving. not everything is always as it seems. is always as it seems. Appearances are deceiving. Things are not always as they seem. Things, even people, are not solely judged on the surface. The things you do, the words you speak, and the silence of your thoughts say a lot about who you are and where you’ve come from. A piece of fruit may prove fresh and clean on the outside, but may turn out rotten and uncannily unkempt on the inside. A piece of jewelry may seem pretty and coveted on the surface, but may soon prove fabricated and fake. To simply judge a book by its cover or to impulsively classify people by the color of their skin never does you any good. If you are too quick to judge and too hasty to comprehend, then judgment will toil and get the best of you. Resentment comes later, and we learn from our mistakes. Yet it is also better and pointedly wiser to practice prudence in thoughts, and patience in both scrutiny and human criticism. Our perspective towards ordinary people who are often subdued by irrational discrimination and stereotypical violence tells a lot about ourselves. The human mind is as subtle as a piece of paper; it is easily swerved and effortlessly influenced, either by moral thoughts or unethical standpoints and failures. Einstein once said, â€Å"Everybody is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid.† If you constantly judge failure after failure and jump hastily into conclusions, people are bound to stagger and take fault after fault as wounds that scar and never heal. They are eventually lead to wallow in depression and self-pity; to wander aimlessly in the void of anxiety and thoughtless failure. You never know how a person does things if you never give them the chance to prove themselves. Everybody is different. We stand out in different ways-at different things. If you fail to give yourself the opportunity to grasp the beauty in their flaws, you need to change yourself. The only factor troubling the equation, the only error that blocks common thought is you and your petty way of thinking. In all honesty, there is nothing wrong with people with defects or disabilities. If negativity arrives and consumes you, then the problem is not them, it’s you-inside you. The sheer lack of comprehension devours anything that’s left. And once it does, reasons are left unnoticed and haplessly ignored. Guy de Maupassant’s â€Å"The Necklace† introduced me to a whole new chapter towards the true meaning of Acceptance. I realized that we can never fully understand what real happiness feels like if we can’t find it within ourselves to let go of our immeasurably high standards in life and accept ourselves for who we are, and what we’ve gone through. Acceptance is about reeling in optimism to forego negativity; it’s about giving up on false hopes and ending broken promises. Life is almost always unfair. We fall down and wallow at depression. We spend too much time focusing on closed doors that we fail to notice the one that’s newly been opened for us. We waste our time meddling with toilsome thoughts on depravity and failure-blinded by both our errors and resentments-that we lose track of what it is that truly matters: the truth. We overshadow the truthiness of our thoughts by allowing self-doubt and conceit to smother us mercilessly. We lose the capacity to think rationally and suffocate in total despair and agony-almost to the point of self-infliction and hate. But Hatred is vindictive. It is spiteful. It is pitiless, and hostile. We lose our chances the moment we lose ourselves. And when we lose our chances-the countless opportunities that have been shed to veer us towards acceptance-we lose at life. It is awfully bitter end, for an awfully bitter life. People are people, and we can never change that. We are subtly driven to maddening influence and suffer relentlessly under the vetoes of hindsight. The human society possesses traits of opposing sides. Half refer to people who have fallen bitterly from grace and think ill of the other half-those who relish in the context of ecstasy and juvenile jubilation; of wonders at liberty of both haste and lustful agitation. Jealousy is unwarranted. It is the birthplace of dysfunctional delusion; the root of hapless paranoia. The human mind easily surrenders to maddening oppression. Obstinate intolerance toils with the frailty of innocence and insensibility. A person is blessed with a myriad of chances and opportunities. A chance to live, a chance to love, a chance to learn, and a chance to grow. But when push comes to shove, oftentimes there’s little we can rummage through; chances are left tainted and severed, and hopes grow unwarranted and shattered. We are fragile little things. When we give up, we break. And when we lose, we fall. To grow a tiny little seedling, it needs to be nurtured and shown affection. To grow an innocent human being, it needs to be loved and shown undivided attention. When we care, it shows. It materializes as words of driven thought-as actions of wholly profound meaning. People who grow dissatisfied and tainted with hatred are people who need guidance and love; an atmosphere that reverberates the echoes of paradise and glory; an area isolated from fear, a place sequestered from sorrow. Dreams come true, and nothing is impossible. Reality might be cruel, but optimism is endless. We fall from grace and deliciate in vainglory-traits unmistakable of derivative human nature yet never inescapable. Happy endings are real, nightmares are short. Life is a bittersweet fantasy-we have our ups, and we have our downs. We fail and we succeed. We fall but strive to stand up. The important thing is to try, and to never stop trying.

Credit Risk Dissertation

Credit Risk Dissertation CREDIT RISK EXECUTIVE SUMMARY The future of banking will undoubtedly rest on risk 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. There 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 â€Å"one size fit all† approach led to a shift, to a new and comprehensive approach -Basel II which adopts a three pillar 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 purpose of CRM which allows fuller offset of security of collateral against exposures by effectively reducing the exposure amount by the value ascribed to the collateral. In this study, a leading 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 track each assets performance in accordance with the evaluation parameters. CHAPTER 1 INTRODUCTION 1.1 Rationale Credit Risk Management in todays deregulated market is a big challenge. Increased market 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, computing maximum permissible limits, assessing management capabilities and prescribing a ceiling for an industry exposure. As banks move in to a new high powered world of financial operations and trading, with new risks, the need is felt for more sophisticated and versatile instruments for risk assessment, monitoring and controlling risk exposures. It is, therefore, time that banks managements equip them fully to grapple with the demands of creating tools and systems capable of assessing, monitoring and controlling 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 balance sheet and instill the need for approaching credit administration from a ‘risk-perspective across the system by placing well drafted strategies in the hands 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 Indian Commercial Banks, particularly in view of; (1) Higher NPAs level in comparison with global benchmark (2) RBI s stipulation about dividend distribution by the banks (3) Revised NPAs level and CAR norms (4) New Basel Capital Accord (Basel -II) revolution 1.2 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 Bank 1.3 RESEARCH METHODOLOGY Research 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 secondary 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 personal 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 internet 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 based on a strict and comprehensive theory but also used market information stock price as an important variance 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. Other 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 safety covenant to protect bondholders. Black and Cox introduce the possibility of more complex capital structures, with subordinated debt. * Geske (1977) introduces interest-paying 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 relevant 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 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 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 default 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 given 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 component 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 component 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 be 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 2 THEORECTICAL FRAMEWORK 2.1 CREDIT RISK: Credit 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 counterparties—from individuals to sovereign governments—and many different types of obligations—from auto loans to derivatives transactions—credit risk takes many forms. Institutions manage it in different ways. Although credit losses naturally fluctuate over time and with economic conditions, there is (ceteris paribus) a statistically measured, long-run average loss level. The losses can be divided into two categories i.e. expected losses (EL) and unexpected losses (UL). EL is based on three parameters:  ·Ã¢â€š ¬Ã‚   The likelihood that default will take place over a specified time horizon (probability of default or PD)  · â‚ ¬Ã‚  The amount owned by the counterparty at the moment of default (exposure at default or EAD)  ·Ã¢â€š ¬Ã‚   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 LGD EL can be aggregated at various different levels (e.g. individual loan or entire credit portfolio), although it is typically calculated 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 note 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 â€Å"cost of doing business† and should therefore be incorporated in loan pricing 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= ÏÆ' (EL) = ÏÆ' (PD*EAD*LGD) Once 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 needs to decide how much capital it wants to hold in order to avoid 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 tail 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, meaning that K is higher and the shape of their loss distribution is more skewed (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 exposure 2.2 The need for Credit Risk Rating: The need for Credit Risk Rating has arisen due to the following: 1. 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 fresh borrowers in addition to monitoring the weaker parameters and taking remedial action. The types of Risks Captured in the Banks Credit Risk Rating Model The 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 risk 2.3 WHY 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 interest: 1. Structural increase in bankruptcies: Although 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. Disintermediation: As capital markets have expanded and become accessible to small and mid sized firms, the firms or borrowers â€Å"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 produced â€Å"a winners† curse effect on the portfolios of traditional FIs. 3. More Competitive Margins: Almost 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: Concurrent 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 worldwide 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 Derivatives: In 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. Technology Advances 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 and the Institute 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 Despite 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 borrowing 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- 3 Credit Risk Approaches and Pricing 3.1 CREDIT RISK MEASUREMENT APPROACHES: 1. CREDIT SCORING MODELS Credit 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 might include 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 MODEL: The 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 of: Zi = ÃŽ £ÃŽ ²jXij + error Where ÃŽ ²j is the estimated importance of the jth variable in explaining past repayment experience. If we then take these estimated ÃŽ ²js 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 MODEL: The 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 original agreement. Chessers model, which was based on a technique called logit analysis, consisted of the following six variables. X1 = (Cash + Marketable Securities)/Total Assets X2 = Net Sales/(Cash + Marketable Securities) X3 = EBIT/Total Assets X4 = Total Debt/Total Assets X5 = Total Assets/ Net Worth X6 = Working Capital/Net Sales The estimated coefficients, including an intercept term, are Y = -2.0434 -5.24X1 + 0.0053X2 6.6507X3 + 4.4009X4 0.0791X5 0.1020X6 Chessers classification rule for above equation is If P> 50, assign to the non compliance group and If P≠¤50, assign to the compliance group. LINEAR DISCRIMINANT MODEL: While linear probability and logit models project a value foe 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 were: X1::Working Capital/ Total Asset X2: Retained Earning/ Total Asset X3: Earning before interest and taxes/ Total Asset X4: Market value of equity/ Book value of total Liabilities X5: Sales/Total Asset The original Z-score model was revised 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 TERM STRUCTURE DERIVATION OF CREDIT RISK: One 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 RATE APPROACH: Rather 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 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 historical default rate in any specific quality class in each year after issue. RAROC MODELS: Based on a banks risk-bearing capacity and its risk strategy, it is thus necessary — bearing in mind the banks strategic orientation — to find a method for the efficient allocation 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 maintenance 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 steps: i) Estimation of asset value and volatility from the equity value and volatility of equity return. ii) Calculation of distance from default iii) Calculation of expected default frequency Credit METRICS: It provides a method for estimating the distribution of the value of the assets n a portfolio subject 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 among 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 simulation 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 appropriate credit spread, t he 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 lender 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 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 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 fol lowing input data: i) Exposure of individual asset ii) Expected default rate iii) Default ate volatilities iv) Recovery rate given default 3.3 CREDIT PRICING Pricing 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 p Credit Risk Dissertation Credit Risk Dissertation CREDIT RISK EXECUTIVE SUMMARY The future of banking will undoubtedly rest on risk 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. There 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 â€Å"one size fit all† approach led to a shift, to a new and comprehensive approach -Basel II which adopts a three pillar 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 purpose of CRM which allows fuller offset of security of collateral against exposures by effectively reducing the exposure amount by the value ascribed to the collateral. In this study, a leading 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 track each assets performance in accordance with the evaluation parameters. CHAPTER 1 INTRODUCTION 1.1 Rationale Credit Risk Management in todays deregulated market is a big challenge. Increased market 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, computing maximum permissible limits, assessing management capabilities and prescribing a ceiling for an industry exposure. As banks move in to a new high powered world of financial operations and trading, with new risks, the need is felt for more sophisticated and versatile instruments for risk assessment, monitoring and controlling risk exposures. It is, therefore, time that banks managements equip them fully to grapple with the demands of creating tools and systems capable of assessing, monitoring and controlling 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 balance sheet and instill the need for approaching credit administration from a ‘risk-perspective across the system by placing well drafted strategies in the hands 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 Indian Commercial Banks, particularly in view of; (1) Higher NPAs level in comparison with global benchmark (2) RBI s stipulation about dividend distribution by the banks (3) Revised NPAs level and CAR norms (4) New Basel Capital Accord (Basel -II) revolution 1.2 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 Bank 1.3 RESEARCH METHODOLOGY Research 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 secondary 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 personal 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 internet 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 based on a strict and comprehensive theory but also used market information stock price as an important variance 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. Other 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 safety covenant to protect bondholders. Black and Cox introduce the possibility of more complex capital structures, with subordinated debt. * Geske (1977) introduces interest-paying 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 relevant 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 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 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 default 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 given 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 component 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 component 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 be 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 2 THEORECTICAL FRAMEWORK 2.1 CREDIT RISK: Credit 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 counterparties—from individuals to sovereign governments—and many different types of obligations—from auto loans to derivatives transactions—credit risk takes many forms. Institutions manage it in different ways. Although credit losses naturally fluctuate over time and with economic conditions, there is (ceteris paribus) a statistically measured, long-run average loss level. The losses can be divided into two categories i.e. expected losses (EL) and unexpected losses (UL). EL is based on three parameters:  ·Ã¢â€š ¬Ã‚   The likelihood that default will take place over a specified time horizon (probability of default or PD)  · â‚ ¬Ã‚  The amount owned by the counterparty at the moment of default (exposure at default or EAD)  ·Ã¢â€š ¬Ã‚   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 LGD EL can be aggregated at various different levels (e.g. individual loan or entire credit portfolio), although it is typically calculated 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 note 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 â€Å"cost of doing business† and should therefore be incorporated in loan pricing 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= ÏÆ' (EL) = ÏÆ' (PD*EAD*LGD) Once 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 needs to decide how much capital it wants to hold in order to avoid 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 tail 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, meaning that K is higher and the shape of their loss distribution is more skewed (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 exposure 2.2 The need for Credit Risk Rating: The need for Credit Risk Rating has arisen due to the following: 1. 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 fresh borrowers in addition to monitoring the weaker parameters and taking remedial action. The types of Risks Captured in the Banks Credit Risk Rating Model The 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 risk 2.3 WHY 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 interest: 1. Structural increase in bankruptcies: Although 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. Disintermediation: As capital markets have expanded and become accessible to small and mid sized firms, the firms or borrowers â€Å"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 produced â€Å"a winners† curse effect on the portfolios of traditional FIs. 3. More Competitive Margins: Almost 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: Concurrent 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 worldwide 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 Derivatives: In 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. Technology Advances 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 and the Institute 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 Despite 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 borrowing 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- 3 Credit Risk Approaches and Pricing 3.1 CREDIT RISK MEASUREMENT APPROACHES: 1. CREDIT SCORING MODELS Credit 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 might include 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 MODEL: The 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 of: Zi = ÃŽ £ÃŽ ²jXij + error Where ÃŽ ²j is the estimated importance of the jth variable in explaining past repayment experience. If we then take these estimated ÃŽ ²js 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 MODEL: The 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 original agreement. Chessers model, which was based on a technique called logit analysis, consisted of the following six variables. X1 = (Cash + Marketable Securities)/Total Assets X2 = Net Sales/(Cash + Marketable Securities) X3 = EBIT/Total Assets X4 = Total Debt/Total Assets X5 = Total Assets/ Net Worth X6 = Working Capital/Net Sales The estimated coefficients, including an intercept term, are Y = -2.0434 -5.24X1 + 0.0053X2 6.6507X3 + 4.4009X4 0.0791X5 0.1020X6 Chessers classification rule for above equation is If P> 50, assign to the non compliance group and If P≠¤50, assign to the compliance group. LINEAR DISCRIMINANT MODEL: While linear probability and logit models project a value foe 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 were: X1::Working Capital/ Total Asset X2: Retained Earning/ Total Asset X3: Earning before interest and taxes/ Total Asset X4: Market value of equity/ Book value of total Liabilities X5: Sales/Total Asset The original Z-score model was revised 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 TERM STRUCTURE DERIVATION OF CREDIT RISK: One 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 RATE APPROACH: Rather 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 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 historical default rate in any specific quality class in each year after issue. RAROC MODELS: Based on a banks risk-bearing capacity and its risk strategy, it is thus necessary — bearing in mind the banks strategic orientation — to find a method for the efficient allocation 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 maintenance 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 steps: i) Estimation of asset value and volatility from the equity value and volatility of equity return. ii) Calculation of distance from default iii) Calculation of expected default frequency Credit METRICS: It provides a method for estimating the distribution of the value of the assets n a portfolio subject 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 among 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 simulation 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 appropriate credit spread, t he 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 lender 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 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 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 fol lowing input data: i) Exposure of individual asset ii) Expected default rate iii) Default ate volatilities iv) Recovery rate given default 3.3 CREDIT PRICING Pricing 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 p