Critical Essay on Cadbury

Cadbury’s Coporate Social Responsibility Businesses these days are much different from how it was in previous generations. Nowadays, society impacts that corporation has is not only about economic power, instead it has also gone into corporate social responsibilities. Cadbury is an international company that is the second largest confectionary company in the world. (Factbox: British confectioner Cadbury 2010).Therefore, they have a bigger impact to affect both positively and negatively on the society as they have a bigger influence and power on the society due to their dominance in market share. In this essay, it will go in depth about the performance of Cadbury in relation to its corporate social responsibility. This essay will explain and argue a balanced argument about the negative and positive impact Cadbury has today on its society by analyzing their â€Å"Cadbury Community† programme and their association with child labour.Negative Social Responsibility of Cadbury According to a documentary called â€Å"Slavery† on the BBC, it documented cocoa beans production and how it is related to child labour, in the documentary, it focused on Cadbury, aiming at them about that negative social responsibility that they have. The reason for child labour in the cocoa production is because of the prices that are set on the cocoa beans is very low when it is sold. For example, farmers are only selling their cocoa beans for only a mere sum of money, therefore they would want to gain more profit.The only way to do that is to get cheaper labour so that their expenses are not so high which would result in higher revenue earned at the end of the day. Since child labour is one of the cheapest labour in the world, it is the top choice for labour to keep cost down would be child labour. In a brighter light, not everyone was affected by the low priced cocoa beans. For example, Cadbury was still able to employ many people around the world and still kept their p roduct prices down to continue attracting their customers.However, Cadbury was later seen as a supporter of child labour. Reason being, Cadbury were purchasing the cocoa beans from the farmers that were using child labour for their cocoa beans production. This in turn makes Cadbury a supporter of child labour as well as they are purchasing the beans from the farmers which encourages them to continue that they are doing. The consumers later came into conclusion that the low prices of Cadbury’s chocolate were not worth the children’s hard cheap labour in the developing countries. Read Critical Essay about Skurzynski’s NethergraveThe world’s largest cocoa producer, Cote d'Ivoire has given the possibility of Cadbury to demand the cocoa beans at a very low price. (World Cocoa Production. n. d. ) As they are the largest producers, they have more control of the cocoa prices around the world. To further exxagerate how much farmers of the cocoa production are getting paid, an example would be, for every kilogram of cocoa beans that a farmer harvest, they are getting paid almost the same amount of how much a bar of chocolate consumers pay for consumption. Which in most cases, would be a range of a dollar to two dollars. (Olivier. 2012. . This is not following their policies that Cadbury should be following under their code of conduct (Our Business Principles. 2008. ). In the document, it states that it is their responsibility, both corporate and social to make sure that there are proper and ethical practices to manage the business. Ethical issues such a s human rights, ethical trading and employment practices are considered when business is done in Cadbury. However, that is not much of the case when Cadbury is purchasing low and unfairly priced cocoa beans from the farmers. This is against their ethical values of ethical trading.Reason being, as mentioned above in this essay, by purchasing the beans at such a low cost, it is encouraging the farmers to hire more child labourers in order to keep their cost of production down and to gain more revenue earned. The stakeholders that are mostly affected would be the children that are forced to work at the farms to harvest the cocoa beans. Working at the farms does not only mean long working hours with very little pay, it also means that they might get beaten often due to carelessness at work or not meeting the expected weight of cocoa beans.It also means that they might not even get paid after working long hours with no food (Cocoa Campaign. n. d. ). By the year 2003, Cote d’Ivoire , which is the world’s largest cocoa producing nation, had about 109,000 child labourers (Country Reports on Human Rights and Practices. 2003). Out of the 109,000 children, more than half of them were said to be working on their own farms owned by their parents. The rest of the children, which consists of about 10,000 of them, are working as slaves or are being trafficked.By working on the farms, it means that the children are not given a chance to go to school to increase their knowledge or to further their education. This would therefore result in a vicious cycle of people depending solely on cocoa farming in order to earn enough money to meet their basic needs. For example, when a child is forced to work on the farms, he will not be able to attend school to gain knowledge to have a chance to get out of the country to work. Since he is stuck on the farm, he will grow up only with the knowledge on how to harvest cocoa beans.His main concern would be to maintain the farm and to earn more money for his family. In order to earn more money, it means that he has to harvest more cocoa beans. Therefore, he will need more help at the farm. Therefore, he will want to get as much help from his children to increase the cocoa beans production. This would continue in a cycle. Cadbury did try to solve the problem that they have made by sourcing their cocoa beans from Ghana, the second largest cocoa producer instead of from Cote d’lvoire. However, many people still are uncertain about their true motives to really solve the problem created.Reason being, back in 2001, the Chocolate Manufacturers Association (CMA) which consisted of large chocolate confectionary companies such as M, Cadbury and Mars Inc. decided to make a promise that their cocoa beans production would be free of child labourers by 2005, July. The commitment was made to the Cocoa Industry Protocol (CIP) (Protocol for Growing and Processing of Cocoa Beans and Their Derivative Products. 2001. ). Al though some large chocolate confectionary companies signed the CIP, none of them were able to meet the criteria of the commitment.Therefore, the dateline was extended and the percentage of their cocoa beans to come from childfree labourers was also reduced. Cadbury has recently self publicized that their products are now labeled as ‘Fair Trade Certified' (About Fairtrade n. d. ) which means that in general perception, a minimum price is to be directly paid to the cocoa producers which would hopefully reduce child labour. However, this is not the case reason being, when farmers are paid the minimum sum of money for their cocoa beans through the Fair Trade premiums, they will still have to minus off the a huge sum of their profit.So what exactly are reducing the farmer’s profit? They are the administrative expenses, operating costs, business reinvestments and other social costs (Fairtrade Certified: Frequently Asked Questions – Advanced n. d. ). Therefore, at the e nd of the day, cocoa farmers are still earning very little. This was just a spin doctoring made by Cadbury to change the public’s perception of Cadbury’s wrong doings. Positive Social Responsibility of Cadbury Cadbury does not only have negative corporate social responsibilities, instead, they are doing well in their work for the local communities around the world.Cadbury has donated some of their profits back to the community. Although this is just a mere 1% of their profit before tax, it is still something as some other companies are not even contributing back to the society at all (Working Together to Make a Difference in the Community n. d. ). Cadbury also has a community that helps in the society’s health, welfare, enterprise, education and environmental sustainability. For example, Cadbury’s â€Å"Miles for Smiles† event involves employees to walk between their two factories and raise funds for to raise funds for the less fortunate.Adding on, Cadbury has also donated to charities, sponsored to countries to help with their developments, developed programmes to help the less fortunate around the world. All these work was done voluntarily by Cadbury. Therefore, it displays the positive side of their company’s social responsibility to give back to the society. Conclusion Although Cadbury has done many negative impacts on the society, they had their fair share of making the world a better place by contributing back to the society as much as they can.Some of the public might still find that Cadbury has a lack of empathy towards ethical issues such as child labour. This might affect Cadbury’s reputation as this would be a hard point to erase form the consumer’s mind. Which means that no matter how much positive things that Cadbury does, at the back of the consumer’s mind, they will always remember the negative impact that Cadbury had caused that is now hard to resolve. And although Cadbury is trying hard to contribute back positively to the society, the public might see is as a way for Cadbury to advertise themselves more.Therefore, in order to keep up the good reputation and try to convert more of the public to view them positively, Cadbury has to keep up with their moral integrity and ethical guidelines, which is seen as a positive action by the public. Work Cited About Fairtrade. n. d. http://www. fairtrade. com. au/about (accessed August 31, 2010) Cocoa Campaign. n. d. http://www. laborrights. org/stop-child-labor/cocoa-campaign (accessed August 30, 2010) Country Reports on Human Rights and Practices. 2003. http://www. state. gov/g/drl/rls/hrrpt/2003/27723. htm (accessed August 30, 2010)Factbox: British confectioner Cadbury. 2010. http://uk. reuters. com/article/idINTRE60D1XX20100114? pageNumber=2=0=true (accessed August 30, 2010) Fairtrade Certified: Frequently Asked Questions – Advanced. n. d. http://www. transfairusa. org/content/resources/faq-advanced. php#indiv iduals (accessed August 31, 2010) Our Business Principles. 2008. http://collaboration. cadbury. com/SiteCollectionDocuments/English%20Booklet. pdf (accessed August 30, 2010) Olivier, M. 2012. Ivory Coast Cocoa Farmers to Put Pay Raise in Crop Output. http://www. bloomberg. om/news/2012-10-05/ivory-coast-cocoa-farmers-to-put-pay-raise-in-crop-production. html (accessed April 2, 2013). Protocol for Growing and Processing of Cocoa Beans and Their Derivative Products. 2001. http://www. cocoainitiative. org/images/stories/pdf/harkin%20engel%20protocol. pdf (accessed August 31, 2010) Working Together to Make a Difference in the Community. n. d. http://www. cadbury. com. au/Cadbury-Community. aspx (accessed August 31, 2010) World Cocoa Production. n. d. http://www. zchocolat. com/chocolate/chocolate/cocoa-production. asp (accessed April 2, 2013).

Ruth St. Denis

Ruth St. Denis Background Ruth St. Denis was born in 1879 in New Jersey to Ruth Emma Denis who was a physician by training. Saint Denis was very strong willed and highly educated. She died in 1968.Advertising We will write a custom essay sample on Ruth St. Denis specifically for you for only $16.05 $11/page Learn More Training St. Denis was encouraged to study dancing at the formative stages of her life. She learnt Delsarte technique in the early stages of her life. Her bullet lessons were conducted by an Italian ballerina Maria Bonfante. She also received training in social dance forms and skirt dancing. Her professional career began in New York in 1892. She worked as a skirt dancer in New York where she performed in dime museums and vaudeville houses. Dime museums traditionally hosted leg dancers who did brief dancing routines. In a day, Ruth did more than eleven brief dance routines. David Belasco spotted Ruth in 1898. By then David was a Broadway producer and a directo r of repute. David then hired Ruth to perform as a featured dancer in his large company. In fact while working with David, Ruth earned her stage name St. Denis which stark with her forever. She was later to be known as Ruth St. Denis. After the tour where ‘Zaza’ was being produced Ruth got to know many important European artists like Sado Tacco and Sarah Benhardt an English actress great of her time. These people positively impacted her life as evidenced by her desire for dance and drama of Eastern cultures. Her interaction with Bernhardt made her like her melodramatic acting style. This later influenced her acting career especially the tragic fate of her character (Sherman, 1983). The technique Ruth St. Denis brought to the fore At the onset of the 20th Century St. Denis began formulating her own theory of dance and drama. These were greatly influenced by the drama techniques she had a brush with early in her dancing training. The theory of dancing was also influenced with her readings on scientology, philosophy , and the history of ancient cultures. The works of Benhardt and Yacco also played a role in defining her theories. In 1904 when she was touring with David Belasco, she came a cross a poster of the goddess of Issis that advertised a cigarette for the Egyptian Deities. This poster overwhelmed her imagination and she later resorted to reading a lot about Egypt and India. St. Denis later quit David Belasco’s company to start her path to the career of a solo artist. It is during this time that she designed her exotic costume and created a story of a mortal maid who was loved by the god of Krishna, Radha. This dance style was premiered in New York’s Vaudeville House. She intended to translate her understanding of the Indian culture and mythology to the American dance stage through Radha.Advertising Looking for essay on art and design? Let's see if we can help you! Get your first paper with 15% OFF Learn More When plying her trade a solo artist Mrs. Orlando Rouland quickly discovered Ruth St. Denis. Ruth St. Denis began performing Radha in Broadway theatres when her wealthy patron started sponsoring her. Ruth had a conviction that Europe had more to offer than any other place would do. That is why in 1906, together with her mother she went to London. She managed to travel in many European cities where she performed a series of translations until 1909. She later returned to New York to give a series of well received concerts in New City when she was touring United States. Up to 1914 she still toured United States dong exotic dance. She was labeled as a classic dancer in the same category with Isadora Duncan despite the fact that they were two different dancers in the perspective of their approach to solo dance. In fact St. Denis sought the universe in the self whereas Isadora Duncan sought the self in the Universe. St. Denis interpreted exotic world through the vantage point of her bod y (Shelton, 1981). After 1911, solo dance on the professional stage faced a eventual death. St. Denis therefore gave lessons to such women like Gertrude Whitney. Her problems were later compounded by the death of her major patron Henry Harris who died on the titanic. Her financial woes forced her back to the studios where she initiated new exotic dance. The difference however was that the new exotic dance had Japanese theme. One of these exotic dance was O-MIKA which was more culturally authentic than her other translations. It was not successful though. This prompted St. Denis to include some other performers in her productions. Ted Shawn came on board in 1914. Ted was a stage dancer who had strong Dalsartean leanings. Hilda Beyer had ballroom preferences. St. Denis continued with her solo translations where as Shawn brought popular dance forms like ragtime and tango. Shawn and Denis later became lovers and dance partners. This partnership marked the end of her career as a career s olo artist (Shelton, 1981). Are they first or second generation pioneers? Ruth St. Denis, Isadora Duncan, and Loie Fuller are considered some of the pioneers of the modern dance. They were against formalism and superficiality of classical academic bullet. These dancers wanted to introduce their audiences to both inner and outer realities.Advertising We will write a custom essay sample on Ruth St. Denis specifically for you for only $16.05 $11/page Learn More Ruth in particular employed pictorial effects that featured in her ritualistic dance of Asian religion. She specifically used elaborate costumes and improvised movements that characterized Egyptian and Indian descent. In fact because of her versatility, she integrated Native American dances and dances from other ethnic groups (Shelton, 1981). Background on their company After her marriage to Shawn in 1914, they together formed Denishawn Company. The company was started in 1915 Los Angelus California. Th rough this company they managed to popularize modern dance throughout the United States and abroad. Through this company talents were nurtured and a second generation of modern dancers was conceived. The second generation dancers that passed through this company were Martha Graham, Doris Humphrey, and Charles Weidman. The Denishawn School of dancing prioritized bullet and experimental bullet dance. The school was first housed in a Spanish style mansion in Los Angelus with spaces for technique classes and Denishawn technique. Technique classes were taken in bare feet and students had to put on one piece black wool bathing piece. The classes ran for three hours each morning. Shawn took the students through stretches, limbering and ballet barre. Floor progressions and free form center combinations were also done by Shawn. St. Denis was in-charge of oriental and yoga techniques. Shawn’s classes were in fact laden with ballet terminology. The classes finally closed with the learni ng of another part of dance. Denishawn trainings were characterized by a theory that one learns to perform by performing and this made a part of concert repertory (Shelton, 1981). Reference List Shelton, S. (1981). Divine Dancer: A Biography of Ruth St. Denis. New York: Doubleday. Sherman, J. (1983). Denishawn: The Enduring Influence. 1. Boston, MA: Twayne Publishers.

Discussion Assignment Example | Topics and Well Written Essays - 250 words - 34

Discussion - Assignment Example This process has been happening naturally as man interacts with the environment. Darwin proves his theory through the size of the brain. Early man had a small brain size approximately 425 cm 3 compared to modern man. Through the fossil records and process of carbon dating, it is easy to follow up the origin of man. The DNA of apes shows a close relationship with that of humans indicating that human beings must have had their origin from apes. Darwin also uses evidence of evolved tools to prove his theory of evolution using fossil records. The early man used sharp stone tools and iron tools. This is evident from data collected by Darwin in caves where the early man lived. According to many anthropologists, man has just evolved recently during the last 50, 000 years providing fresh evidence on evolution. The change in technology, language, culture, and specialized lithic technology has changed gradually changed human

Tom's of Maine Toothpaste Branding Research Paper

Tom's of Maine Toothpaste Branding - Research Paper Example The paper outlines the benefits of Toms of Maine Toothpaste, how it relates to the target market and how the firm can use packaging and labeling to support its brand image. Toms of Maine toothpaste has many attributes and benefits. Specifically, the Toms of Maine botanically bright toothpaste bears distinct characteristics from the other toothpastes. It is a natural brand in the toothpaste market that whitens teeth and freshens breath. In addition, it can remove plague using ingredients derived from nature. Silica is one of its ingredients, and it contributes immensely in whitening the teeth. The brand incorporates exclusive blends or mixture of soothing botanicals that makes it a top quality product. Lastly, it lacks fluoride and paraben, and this makes it safer than the other toothpaste brands. The attributes and benefits of Toms of Maine toothpaste relate to the target market as it satisfies the demands of customers who dislike products containing artificial additives linked with the causation of cancer. These groups of individual have formed a market niche that Toms of Maine targets with its new brands that are free from artificial preservatives. For instance, Toms of Maine botanically bright toothpaste targets such upcoming market niches. In addition, There is a large market of customers suffering from the plague, bad breath, tarnishing teeth and other mouth conditions. The benefits and attributes outlined can give answers to these problems. Therefore, the attributes and benefits of Toms of Maine toothpaste serves to satisfy a large market that demands its sure impacts. Toms of Maine can use labeling and packaging to protect and promote the product as well as to provide additional value and aspects of differentiation. The three functionalities are instrumental in maintaining the image of the brand (Hirschman, 2010). The firms can ensure that it uses the right material for packaging its toothpastes. An excellent package protects the product

Roles and responsibilities Essay Example | Topics and Well Written Essays - 2000 words

Roles and responsibilities - Essay Example Coaching and physical instruction involves programming and planning process. It extensively involves the teaching style, the learning style, the leadership style, the coaching style and the communication skills. According to Cross (1999), physical fitness is categorized into two, first, is the general fitness which refers to the condition of an individual health and wealth being. Secondly, is the specific fitness which is mainly task-oriented. It is defined depending on an individual’s ability to carry out different aspects of sports. Physical fitness is gained through exercising, having the correct nutrition and adequate rest. All these are important in an individual’s life. According to Weinberg and Gould (2005), physical activity is an exercise through which the body is made to work extra hard than normal. It involves actives that go to the extreme level as compared to one’s routine of just sitting, standing and walking up the stairs. Increased Physical activi ty is beneficial to all. Sport is known to be a game that has its basis in physical athleticism, (Heyward, 2006). The roles and responsibilities of a coach are viewed at times as being complex and involving Cassidy, (2005). At the same time they are exciting and very rewarding to all individuals involved. ... This is based on the idea that reassurance and relieve is attained through sharing anxieties. Fourthly, a coach is a demonstrator; a coach has to clearly demonstrate the right skill which the athletes are supposed to perform. Fifth, he or she plays a role of a friend; a coach and an athlete develop personal relationship with time as they work together. Apart from provision of coaching advice sport coach become a close person who can also be involved problems discussion and sharing of success. A coach has to be careful and ensure that all personal information remains confidential. Through this, the coach will manage to maintain the existing friendship and respect. The sixth role is that of a facilitator, a coach is greatly involved in identifying the appropriate competitions which best suit the athletes. This will assist the competitors in attaining their yearly objectives. The seventh role is that of a fact finder, a coach plays a key role in collection of data of both national and i nternational results and provides updates with the latest training techniques. Eight, a coach is a fountain of knowledge; in some cases coaches are asked questions on different events on media, for example television, diet, sport injuries and other topics outside the field of sports. Ninth, a coach is also an instructor who is supposed to instruct athletes on different sport skills. Tenth, he or she is a motivator; a coach plays a key role in maintaining the motivation of athletes throughout the year. Twelfth, he or she is a role model, a coach remains to be a model on specific behavioural and social role for those under him or her to imitate. This is among the most important roles as coaches are required to be good examples to

Film responses 13 Movie Review Example | Topics and Well Written Essays - 500 words

Film responses 13 - Movie Review Example The jump cut shows Antoine in the bathroom. He wipes the mirror and there is a voice-over of the teacher saying: â€Å"I deface the classroom walls.† The voice-over is a distancing technique. It helps people to think about the kind of boy that Antoine is and the kind of life that he has than feel for him as a delinquent. When his father appears in the mirror to show his socks with many holes, it shows the theme of mixing genres, of including comedy in a dramatic moment. This is part of the auteur theory where Truffaut includes small things that matter to a leisure narrative development, especially the wit and charm of the characters. Try to make a point of not choosing opening scenes or scenes that are featured on You Tube.When you find a scene that clearly shows French New Wave technical & thematic elements--note those elements as you  describe each scene in vivid detail--using film terms whenever appropriate.  Remember--its always easiest to work your way chronologically through the scene--describing what you see as the narrative unfolds.   Important--Make sure you also extend your description into a discussion of the purpose and/or effect of various technical or elements of mise-en-scene choices. The assignment this week will help prepare you for next weeks

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