Dunkirk and the Battle of Britain Essay

Sources A, B and C all give information about what happened at the battle of Dunkirk and about the evacuation. All three sources were written by British people which means that the sources could be biased or contain incorrect information. Source A was written by Commander Thomas Kerr, a naval officer sent to organise the evacuation. Since the naval officer is British we can speculate that the source is biased. Thomas Kerr starts off by saying â€Å"What a terrible night that was, for we had got hold of the odds and ends of an army, not the fighting soldiers.†- this suggests to us that it was a horrible night, the soldiers they had got hold of were like the leftovers from the battle, they were depressed, hungry and they had low morale we know that this could be true because in the DVD we saw in class called ‘The Finest Hour’, a documentary about the evacuation of Dunkirk from a BBC television series, we see soldiers like Peter Vaux who hadn’t eaten for 5 days and was very tired. This source tells us that they weren’t fighting soldiers which we can say is true because if all the suggestions above are true, we can truly conclude that the soldiers weren’t ready to fight. Thomas Kerr goes on to say â€Å"There were hardly any officers, and the few present were useless†- this implies that there were hardly any officers left because they could have been killed or captured during the evacuation, and for the ones that were their they were probably so taken aback by the events going on that they could no longer instruct. It could also tell us that the officers there were tired or injured, so they couldn’t do much. This could be biased because Thomas Kerr could be trying to make us think that the officers were useless to try and make him or other naval commanders look good. Thomas Kerr then says â€Å"but our promise of safety, and the sight of our naval uniforms, resorted some order to the rabbleâ€Å"- this tells us that the navy was promising the soldiers safety and that their ‘glorious’ uniforms resorted some order to the rabble, this can be seen as biased because Thomas Kerr is making us think that the navy was powerful and saying it as if though the navy were the leaders, he himself is a part of the navy and just wants to make them look superior and heroic. Thomas Kerr lastly says â€Å"Their faith in the navy was pathetic; we could only do our best.†- this suggests that the soldiers didn’t really believe in the navy that they thought of them as they did everyone else, it then suggests that they could only do their best. Overall, this source gives us a lot of information on what happened in the evacuation of Dunkirk although quite a bit of the information is biased, in favour of the British and the navy there is some truth in the source.

The Hunters: Phantom Chapter 7

â€Å"Who's Celia?† Bonnie said indignantly, as soon as they'd wiped off the blood. She'd put the rose down careful y in the middle of the front seat, between her and Matt, and they were al very consciously not touching it. Pretty as it was, it looked more sinister than beautiful now, Stefan thought grimly. â€Å"Celia Connor,† Meredith said sharply. â€Å"Dr. Celia Connor. You saw her in a vision once, Bonnie. The forensic anthropologist.† â€Å"The one who's working with Alaric?† Bonnie said. â€Å"But why would her name show up in blood on my arm? In blood.† â€Å"That's what I'd like to know,† Meredith said, frowning. â€Å"It could be some kind of warning,† Elena proposed. â€Å"We don't know enough yet. We'l go to the station, we'l meet Alaric and Celia, and then†¦Ã¢â‚¬  â€Å"Then?† prompted Meredith, meeting Elena's cool blue eyes. â€Å"Then we'l do whatever we have to do,† Elena said. â€Å"As usual.† Bonnie was stil complaining when they got to the train station. Patience, Stefan reminded himself. Usual y he enjoyed Bonnie's company, but right now, his body craving the human blood he'd become accustomed to, he felt†¦ off. He rubbed his aching jaw. â€Å"I'd real y hoped we'd get at least a couple days of everything being normal,† Bonnie moaned for what seemed like the thousandth time. â€Å"Life's not fair, Bonnie,† Matt said gloomily. Stefan glanced at him in surprise – Matt was usual y the first to leap in and try to cheer up the girls – but the tal blond was leaning against the closed ticket booth, his shoulders drooping, his hands tucked into his pockets. Matt met Stefan's gaze. â€Å"It's al starting up again, isn't it?† Stefan shook his head and glanced around the station. â€Å"I don't know what's going on,† he said. â€Å"But we al need to be vigilant until we can figure it out.† â€Å"Oh, that's comforting,† Meredith muttered, her gray eyes alertly scanning the platform. Stefan folded his arms across his chest and shifted closer to Elena and Bonnie. Al his senses, normal and paranormal, were on ful alert. He reached out with his Power, trying to sense any supernatural consciousnesses near them, but felt nothing new or alarming, just the calm background buzz of ordinary humans going about their everyday business. It was impossible to stop worrying, though. Stefan had seen many things in his five hundred years of existence: vampires, werewolves, demons, ghosts, angels, witches, al sorts of beings who preyed on or influenced humans in ways most people could never even imagine. And, as a vampire, he knew a lot about blood. More than he had cared to admit. He'd seen Meredith's eyes flick toward him with suspicion when Bonnie began to bleed. She was right to be wary of him: How could they trust him when his basic nature was to kil them? Blood was the essence of life; it was what kept a vampire going centuries after his natural life span should have ended. Blood was the central ingredient in many spel s both benevolent and wicked. Blood had Powers of its own, Powers that were difficult and dangerous to harness. But Stefan had never seen blood behave in the way it had on Bonnie's arm today. A thought struck him. â€Å"Elena,† he said, turning to face her. â€Å"Hmmm?† she answered distractedly, shading her eyes as she peered down the track. â€Å"You said the rose was just lying there waiting for you on the porch when you opened the door this morning?† Elena brushed her hair out of her eyes. â€Å"Actual y, no. Caleb Smal wood found it there and handed it to me when I opened the door to let him in.† â€Å"Caleb Smal wood?† Stefan narrowed his eyes. Elena had mentioned earlier that her aunt had hired the Smal wood boy to do some work around the house, but she should have told him of Caleb's connection to the rose before. â€Å"Tyler Smal wood's cousin? The guy who just showed up out of nowhere to hang around your house? The one who's probably a werewolf, like the rest of his family?† â€Å"You didn't meet him. He was perfectly fine. Apparently he's been around town al summer without anything weird happening. We just don't remember him.† Her tone was breezy, but her smile didn't quite reach her eyes. Stefan reached out automatical y to speak to her with his mind, to have a private conversation about what she was real y feeling. But he couldn't. He was so used to depending on the connection between them that he kept forgetting it was gone now; he could sense Elena's emotions, could feel her aura, but they could no longer communicate telepathical y. He and Elena were separate again. Stefan hunched his shoulders miserably against the breeze. Bonnie frowned, the summer wind whipping her strawberry ringlets around her face. â€Å"Is Tyler even a werewolf now? Because if Sue's alive, he didn't kil her to become a werewolf, right?† Elena held her palms to the sky. â€Å"I don't know. He's gone, anyway, and I'm not sorry. Even before he was a werewolf, he was a real jerk. Remember what a bul y he was at school? And how he was always drinking out of that hip flask and hitting on us? But I'm pretty sure Caleb's just a regular guy. I'd have known if there was something wrong with him.† Stefan looked at her. â€Å"You've got wonderful instincts about people,† he said careful y. â€Å"But are you sure you're not relying on senses you don't have anymore to tel you what Caleb is?† He thought of how the Guardians had painful y clipped Elena's Wings and destroyed her Powers, the Powers she and her friends only half-understood. Elena looked taken aback and was opening her mouth to reply when the train chugged into the station, preventing further discussion. Only a few people were disembarking at the Fel ‘s Church station, and Stefan soon spotted Alaric's familiar form. After stepping down to the platform, Alaric reached back to steady a slender African-American woman as she exited behind him. Dr. Celia Connor was certainly lovely – Stefan would give her that. She was tiny, as smal as Bonnie, with dark skin and close-cropped hair. The smile she gave Alaric as she took his arm was charming and slightly puckish. She had large brown eyes and a long, elegant neck. Stylish but practical in designer clothing, she wore soft leather boots, skinny jeans, and a sapphire-toned silk shirt. A long, diaphanous scarf was wrapped around her neck, adding to her sophisticated demeanor. When Alaric, al tousled sandy hair and boyish grin, whispered familiarly in her ear, Stefan felt Meredith tense. She looked like she'd like nothing better than to try out a few of her martial arts moves on a certain gorgeous forensic anthropologist. But then Alaric spotted Meredith, dashed over, and took her in his arms, pul ing her off her feet as he swung her into a hug, and she visibly relaxed. In a few moments, they were both laughing and talking, and they didn't seem to be able to stop touching each other, as if they needed to reassure themselves that they were actual y together again at last. Clearly, Stefan thought, any worries Meredith had had about Alaric and Dr. Connor had been groundless, at least as far as Alaric was concerned. Stefan turned his attention to Celia Connor again. His first wary tendrils of Power discovered a slight simmering resentment emanating from the anthropologist. Understandable: She was human, she was quite young despite her poise and her many professional achievements, and she had spent a great deal of time working closely with the very attractive Alaric. It wouldn't be surprising if she felt a bit proprietary toward him, and here he was being pul ed away from her and into the orbit of a teenage girl. But more important, his Power found no supernatural shadow hanging about her and no answering Power in her. Whatever the meaning of the name Celia written in blood, it seemed Dr. Celia Connor hadn't caused it. â€Å"Somebody take pictures!† Bonnie cal ed, laughing. â€Å"We haven't seen Alaric for months. We have to document his return!† Matt got out his phone and took a couple of pictures of Alaric and Meredith, their arms around each other. â€Å"Al of us!† Bonnie insisted. â€Å"You too, Dr. Connor. Let's stand in front of the train – it's a terrific backdrop. You take this one, Matt, and then I'l take some with you in them.† They shuffled into various positions: bumping, excusing, introducing themselves to Celia Connor, throwing their arms around one another in a casual y exuberant style. Stefan found himself pushed to the edge, Elena's arm through his, and he discreetly inhaled the clean, sweet scent of her hair. â€Å"Al aboard!† the conductor cal ed, and the train doors closed. Matt, Stefan realized, had stopped taking pictures and was staring at them, his blue eyes widening in what looked like terror. â€Å"Stop the train!† he shouted. â€Å"Stop the train!† â€Å"Matt? What on earth?† Elena said. And then Meredith looked behind them, toward the train, with an expression of dawning comprehension. â€Å"Celia,† she said urgently, reaching out toward the other woman. Stefan watched in confusion as Celia jerked away from them abruptly, almost as if an unseen hand had grabbed her. As the train began to move, Celia walked, then ran beside it with stiff, frantic motions, her hands pul ing rapidly at her throat. Suddenly Stefan's perspective shifted and he understood what was happening. Celia's diaphanous scarf had somehow been firmly caught by the closing door of the train, and now the train was pul ing her along by the neck. She was running to keep from being strangled, the scarf like a leash yanking her along. And the train was beginning to pick up speed. Her hands pul ed at the scarf, but both ends were caught in the door, and her tugging only seemed to tighten it around her neck. Celia was approaching the end of the platform and the train was chugging faster. It was a flat drop from the platform to the scrub ground beyond. In a few moments, she would fal , her neck would be broken, and the train would drag her along for miles. Stefan took al this in within the space of a single breath and sprang into action. He felt his canines lengthen as a surge of Power went through him. And then he took off, faster than any human, faster than the train, and sped toward her. With one quick motion, he took her in his arms, relieving the pressure around her throat, and tore the scarf in half. He stopped and put Celia down as the train sped up and left the station. The remnants of the scarf slipped from around her neck and fluttered onto the platform by her feet. She and Stefan stared at each other, breathing hard. Behind them, he could hear the others shouting, their feet pounding on the platform as they ran toward them. Celia's dark brown eyes were wide and fil ed with tears of pain. She licked her lips nervously and took several short, gasping breaths, pressing her hands against her chest. He could hear her heart pounding, her blood rushing through her system, and he concentrated on pul ing his canines back and resuming his human face. She staggered suddenly, and Stefan slipped his arm around her. â€Å"It's okay,† he said. â€Å"You're al right now.† Celia gave a short, slightly hysterical laugh and wiped at her eyes. Then she stood upright, straightening her shoulders, and inhaled deeply. Stefan could see her deliberately calming herself, although her heartbeat was reeling, and he admired her self-control. â€Å"So,† she said, holding out her hand, â€Å"you must be the vampire Alaric's told me about.† The others were coming up to them now, and Stefan glanced at Alaric in alarm. â€Å"That's something I'd rather you kept private,† Stefan told her, feeling a prick of irritation at Alaric for divulging his secret. But his words were almost drowned out by a gasp from Meredith. Her gray eyes, usual y so serene, were dark with horror. â€Å"Look,† she said, pointing. â€Å"Look at what it says.† Stefan turned his attention to the pieces of sheer fabric around their feet. Bonnie gave a little whimper and Matt's eyebrows furrowed. Elena's beautiful face was blank with shock, and Alaric and Celia both appeared entirely confused. For a moment, Stefan saw nothing. Then, like a picture coming into focus, his vision adjusted and he saw what everyone was looking at. The torn scarf had fal en into an elaborately twisted heap, and the supposedly random folds of fabric quite clearly formed letters that spel ed: meredith

Cipd Assignment Example | Topics and Well Written Essays - 1500 words

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INTERNATIONAL HRM Assignment Example | Topics and Well Written Essays - 2000 words

INTERNATIONAL HRM - Assignment Example As a result, the concept has evolved to be wider and more complex to be defined within limited words. It has also expanded its dimensions by covering cultural, political, technological and economical divergences in order to determine appropriate set of objectives and strategies in order to attain them. Therefore, HRM practices differ from one nation and geographical region to another. With this belief, the discussion of the paper will emphasise on identifying various dimensions of International HRM in the context of the US. The objective of the paper is to evaluate the impact of political, economical, social and technological factors existing on the HRM practices in the US. Moreover, the paper will critically evaluate and analyse two key areas of HRM in modern day phenomenon, i.e. training & development and performance appraisal applied by a US based multinational company, i.e. Walmart. 2.0. International HRM In recent years, the world has witnessed steep rise in international activi ties which forced the managerial dimension to cross the regional boundaries and enter the international market. For instance, evidences reveal that since the early 20th century, international trade and production operations have increased substantially. This forced national companies to expand their business in the global platform recognising them as Multinational Companies (MNCs). Consequently, there are also few challenges which are faced by MNCs while implementing their HRM practices in the international platform. However, managing dichotomous cultures and economic factors with efficiency is termed to be the most challenging tasks of International HRM (IHRM) (Tayeb, 2005). Similar set of challenges were also faced by the US based MNCs in their global operations. Studies reveal that during early period, most of the US based international companies witnessed failure in their global operations, especially in managing their human resources with efficacy due to lack of effectiveness i n the HRM policies. In other words, the companies were concerned in practicing similar kind of HRM policies in the international platform as it did in the national context, i.e. in the US. Consequently, the practices lacked affectivity and efficacy and thus faced the challenges in terms of expatriation (Ashamalla, 1998). In this context, numerous researches have been performed to identify the effect of global and local factors in determining the efficacy of IHRM practices by the US based international companies. Most of the researches stated that the IHRM practices and policies of the companies operating globally often tended to provided greater significance to the local factors without entirely ignoring the prospects of their parent companies. Evidences have also revealed that different factors existing in the external environment influences the IHRM practices. For instance, the IHRM practices and policies executed by the Japanese leaders globally tend to be highly focussed on the localised factors in terms of ‘corporate welfarism’, but in a tailored manner to adopt the changes present in the targeted market while training the expatriates. Likewise, the IHRM policies and practices implemented by the US subsidiaries tend to be based on developmental strategies to a large extent with a similar concern. Therefore, it becomes quite apparent that both the international and national factors influence the IHRM practices

Recuitment and Selection Process Essay Example | Topics and Well Written Essays - 1000 words

Recuitment and Selection Process - Essay Example The new employees must be able to meet the standards for innovation in law enforcement (Ackerman, 2009). Special Agents To achieve its objectives, the FBI looks for highly motivated men and women who posses the intelligence, skills and integrity necessary to be a special agent. Special Agent candidates are required to have a Bachelor’s degree in any discipline and three years of full-time work experience. In addition, citizens can also qualify to be an FBI agent with a degree in law, accounting, engineering, computer science, or any 4-year degree and fluency in a foreign language needed by the FBI. The main languages that are considered include: Spanish, Russian or Chinese (Fbijobs.gov, 2012). Qualification Requirements for Police Officers The FBI recruits any US citizen of 21 years and above. In addition, the prospective agents must have a valid driver’s license as well as pass the FBI background investigation. The candidates are then required to receive a top secret s ecurity clearance. There are various requirements to qualify as an FBI police officer. However, there are educational and job related experience that is needed in every position. Experienced police officers are allowed to join the FBI at grade GS6, GS 7 or GS 8 grade levels. Other police officer who do not have specialized work experience can only join the FBI at grade GS 5. ... In addition, the FBI allows police officers to submit their resumes directly to the police recruiters. After the application process, the recruits are then subjected to a written test and panel interview (Fbijobs.gov, 2012). 2. Phase I Testing After the best candidate for the position has been selected, they are then contacted and scheduled for testing. Written tests are given to the successful applicants. In addition, panel interview is also conducted on the applicants. The writing test normally takes place at the FBI facility. Tests consist of two processes. The first process involves a written and video portion. Process two involves a panel interview which is conducted by active duty FBI police officers. Candidates who qualified through foreign language requirements are given additional test to determine their proficiency in the foreign languages (Fbijobs.gov, 2012). 3. Phase II testing A candidate must be selected based on their competitiveness, and the requirements of the FBI. T hese candidates also qualify for a conditional Job offer. The FBI selects candidates based on their budgetary constraints and skills required to perform the duties. The FBI officers ensure that successful applicants know the job title, GS law enforcement pay grade and salary (Fbijobs.gov, 2012). 4. The FBI Background Investigation Successful candidates who have been able to get a conditional job offer at the FBI are required to provide their personal information to the Equip system. In addition, the candidates are required to have a Top secret clearance before they begin the background investigation. During the background test, candidates are expected to go through a polygraph investigation procedure as well as through credit

Contrast and compare the psychological theories of Jung and Freud Essay

Contrast and compare the psychological theories of Jung and Freud. Discuss some implications of their theoretical and therapeutic differences - Essay Example As seen in the research conducted by Schimmel (2013, pp. 61-77), Sigmund Freud was of the opinion that the mind comprise of three levels that control the human. The first level is the unconscious mind, second the preconscious mind and lastly the unconscious mind. For the case of the Psychoanalytic theory, focus is mostly put on the unconscious mind. In the work of Schimmel (2013, pp. 61-77), the unconscious mind entails feelings and thoughts such as sexual feelings, uninvited thoughts, events as well as the experiences that are in contrary with the conscious mind. In the thought of Freud, personality development solely relied on the unconscious motivations of the self (Rycroft, 1995, pp. 38-52). Freud also noted that the mind contains, the id, ego and the super ego (Schimmel, 2013, pp. 61-77). The id seeks to attain pleasure and will not stop until it attains its immediate satisfaction. In the event that the id does not get satisfied, it automatically turns aggressive. The ego tries to fulfil the desires of the id. This explains that the ego works under pressure from id to meet its needs if long term satisfaction has to be attained. The facilitation of the needs between the desires of id and ego reduces the chances of aggression and chaos resulting (Schimmel, 2013, pp. 61-77). Lastly, the super ego is a conscious part of the mind that represents the expected norms and values of the society. Schimmel (2013, pp. 61-77) writes that the superego determines what is factual and what is real; thus, determine how a person ought to behave in the society. Simply put, the superego determines what is right and what is not in the society more so as prescribed by one’s parents as well as the environment. It is through the superego that persons determine if they are right or wrong. In the long run, individuals end up appreciating themselves or even becoming guilty of their actions. The function of the superego is indeed a contrary of the id and the ego

Target Market and Marketing Strategy Assignment

Target Market and Marketing Strategy - Assignment Example The second part of the assignment elaborates a product overview which is created and the target market is developed with respect to the product. The product is created keeping in mind the market demand of United States (US). The product being washing powder or laundry detergent is highly used in every household of US. The product which is created is named as White Magic and its features are created keeping in mind the other competitor’s detergent features so that it is fit for US market. Lastly, a competitor analysis is done to identify the biggest player in the US detergent market. Abraham Maslow is known for proposing the Hierarchy of Needs Theory in 1943. Maslow’s theory of needs reflects the basic human requirements and highlights the scale of motivations that result in desired outcomes. The theory explains that the motivation for any action made by any human being is an unfulfilled need. It identifies five primary areas of needs that are experienced by most humans. The five levels are a physiological need, safety needs, social needs, esteem needs and lastly the self-actualization needs (Daina, 2007). Maslow stated that the human behavior and decision-making are guided by one of the five need levels in his hierarchy that are discussed above. Now apply this theory to the marketing concept it can be implied that consumer targets can reflect a basis of perspectives and the decisions that come with those perspectives. The main determinant of potential success is the ability to effectively appeal to one the above motivational drivers (Thompson, 2013). When a company is selling a product to a customer they are not only selling the product but also selling the idea and the image of the product to the customer. The companies promise to fulfill one or more needs in the hierarchy. Thus the marketing campaign plays an important role in marketing a product. A marketing campaign in order to be successful should motivate the people who are at lower levels of needs. Knowing consumer taste is the key to a successful company.

Brand Essay Example | Topics and Well Written Essays - 500 words

Brand - Essay Example Now it belongs to Goga Ashkenazi, a Kazak former oil and gas tycoon, whose attraction to fashion industry and to Vionnet brand in particular was dictated by a belief in heritage and history of brand (Haritela, 2013). Despite the entire couture industry is directed toward meeting the desires of those customers who can afford luxury clothes, in terms of affordability, the creative director of Vionnet and its owner Goga Ashkenazi is striving to capture more consumers by creating the same couture in a more accessible way (Karmali, 2013). While clothes of couture range sometimes from tens to hundreds of thousands of pounds for the dress, it becomes less possible to all most women to purchase such dress and under such tough economic conditions, it is practically impossible. For that reason, Vionnet offers demi-couture gowns that is full of beauty and captures the couture creation, however, is more affordable for the customers. In addition, couture dresses are usually one-time event clothes, while Vionnet wants to provide its consumers those dresses that will be suitable to wear in more ways than one. Thus, a dress from Vionnet for  £2,000 is considered as affordable couture unit available in differe nt colours, prints and material, and which at the same time does not also lose it exclusivity and unique feature of haute couture. The successful revitalization of Vionnet brand has is also influenced by its designer Chalayan, who is more known for the visionary magician. In U.S. the first new Vionnet collection became available in the house atelier within Barneys New York flagship stores. In Milano the first modern boutique of Vionnet brand was presented in 2011. Today the brand is present in more than hundred and eighty stores worldwide. According to Vogue (Karmali, 2013), the modern collection for Vionnet became also available to order in-store at Harrods. In addition, one can make a purchase through online

The Future is Personalized Medicine Essay Example | Topics and Well Written Essays - 1000 words - 1

The Future is Personalized Medicine - Essay Example Pharmacogenomics and pharmacogenetics, which are expected to be at the core of PM, combine to offer several advantages over conventional clinical methods. For example, while pharmacogenomics is limited to identifyng 'inheritable response' to medication across the whole genome, pharmacogenetics studies the effects of medication at the level of individual genes (the chart below outlines how it operates). The former also tries to reveal important links between genomic patterns and clinical responses. Such links are crucial sources of medical knowledge, as they empower clinicians to choose a particular treatment option based on individual patient condition as opposed to adopting a formulaic trial-and-error approach. (Hood, 2003, p.582) The Human Genome Project (HGP), initiated and supported by former American President Bill Clinton was pivotal to subsequent breakthroughs in Personalized Medicine. With the help of advanced computing power, already more than 3 billion base pairs of DNA hav e been successfully mapped. With the completion of the HGP in 2003, new possibilities for PM have been opened up. Working expeditiously to make Personalized Medicine a reality in the near future are such organizations as the International HapMap Project, the NIH Encyclopedia of DNA Elements (ENCODE), the Roadmap Epigenomics Program, etc. (Cox, et. al., 2007, p.112) In order to understand the scope and effectiveness of Personalized Medicine, let us take a hypothetical case. For instance, in the case of oncology treatment, where presently oral-intake medicines can cost hundreds (if not thousands) of dollars per year for the afflicted patient, understanding the intricate 'genetic pathways' (which is unique to each individual) is important in order to determine the probable efficacy of a particular therapy course. It is a sign of progress that in the United States today â€Å"there are 6 drugs for which FDA requires diagnostic genetic testing before prescription, about 30 for which a d iagnostic test is recommended, and another 200 with pharmacogenomic information on the labels.† (Hesselgrave, 2010, p.16) Moreover, traditional healthcare provision adopts an one-size-fits-all approach. Whereas, under Personalized Medicine, this practice will be dismantled and unique prognostic courses would be designed based on the patient's genetic make up. Powerful technologies that try to understand the working of the human body down to the cellular level will be part of future PM treatments. These technologies include genomics, proteomics (the study of proteins), and metabolomics (the study of metabolites) amongst others. Metabolomics is of special interest to physicians as it has the potential to provide key information about individual patients. While the purpose of proteomics research is to identify abnormal protein patterns in patients, the purpose of metabolomic research is to identify abnormal metabolite patterns. Scientists are of the view that human bodies contain more than 3,000 metabolites that play a crucial role in proper growth and development of various organs. Apart from these primary metabolites there are secondary metabolites which strenghthen the immune system and helps reduce mental and physical stress. Of particular interest to the medical community are low-molecular-weight metabolites

Student Essay Example for Free

Student Essay Diversity  refers to the differences between individuals. People differ on all kinds of aspects, both visible and non-visible. Examples of differences are gender, age, skills, tenure, learning styles etc. We find these differences in every workplace, though not all differences are always recognised or seen as relevant. Unity in diversity Unity in diversity  is a concept of unity without uniformity and diversity without fragmentation  that shifts focus from unity based on a mere tolerance of physical, cultural, linguistic, social, religious, political, ideological and/or  psychological  differences towards a more complex unity based on an understanding that difference enriches human interactions. The concept of unity in diversity was used in non-Western cultures such as indigenous peoples in North America and Taoist societies in 400-500 B. C. In premodern Western culture it has been implicit in the organic conceptions of the universe that have been manifest since the ancient Greek and Roman civilizations through medieval Europe and into the Romantic era. Diversity of religion in India Throughout Indias history,There had been many religions which  religion  has been an important part of the countrys culture. Religious diversity andreligious tolerance  are both established in the country by the  law  and  custom. A vast majority of Indians, (over 93%), associate themselves with a religion. According to the 2001 census,[1]  80. 5% of the  population of India  practice  Hinduism. Islam  (13.4%),  Christianity  (2. 3%),  Sikhism  (1. 9%),  Buddhism(0. 8%) and  Jainism  (0. 4%) are the other minor religions followed by the people of India. Languages of India The  Languages of India  belong to several  language families, the major ones being the  Indo-Aryan languages  (a subbranch of Indo-European) spoken by 74% of Indians and the  Dravidian languages  spoken by 23% of Indians. Other languages spoken in India belong to the  Austroasiatic,  Tibeto-Burman, and a few minor language families and  isolates. The official language of the Central Government of  Republic of India  is  Standard Hindi, while  English  is the secondary official language. Cuiture of India Indian cultural history spans more than 4,500 years. During the  Vedic period  (c. 1700–500 BCE), the foundations of  Hindu philosophy,  mythology, and  literature  were laid, and many beliefs and practices which still exist today, such as  dharma,  karma,  yoga, and  mok? a, were established. India is notable for its  religious diversity, with Hinduism, Sikhism, Islam, Christianity, and Jainism among the nations major religions. The predominant religion, Hinduism, has been shaped by various historical schools of thought, including those of the  Upanishads,  the  Yoga Sutras, the  Bhaktimovement,  and by  Buddhist philosophy. Indias  languages,  religions,  dance,  music,  architecture,  food, and customs differ from place to place within the country. The Indian culture, often labelled as an amalgamation of several cultures, spans across the  Indian subcontinent  and includes traditions that are several millennia old. Many elements of Indias diverse cultures, such as  Indian religions,  yoga, and Indian cuisine, have had a profound impact across the world. Difference between Andhra Pradesh and Tamil Nadu Andhra Pradesh and Tamil Nadu are two south Indian states. Both are located in the peninsular Deccan plateau, bounded by the Bay of Bengal to the east. Andhra is India’s fourth largest state by area and has the second longest coastline amongst all of the Indian states. AP’s capital is Hyderabad and the official language is Telugu. Tamil Nadu is the southernmost part of Indian peninsula and is the eleventh largest state by area. Since 500 BC it has been the home of the Tamil people with Chennai (Madras) is its capital city. TN is the most urbanized state in India and claims eight UNESCO World Heritage Sites. Economy In comparison to its share of population, Tamil Nadu has the highest number of enterprises in India and is the fifth largest contributor to India’s Gross Domestic Product. It is responsible for the third largest Indian economy in 2008 and is also the most industrialized state in India. TN holds third position on the list of states with the most Foreign Direct Investment (FDI) approvals and has a network of over 100 industrial parks. Andhra Pradesh is called the rice bowl of India and agriculture is its main source of income, however, it is rapidly growing in the fields of Information Technology and biotechnology. In terms of mineral wealth, Andhra Pradesh holds second position and accounts for one third of the total limestone reserves in the country. Tourism Tamil Nadu has the second largest tourism industry in India and it is growing at a rapid rate of around 16%. It is controlled by TTDC- Tamil Nadu Tourism Development Corporation. TTDC promotes tourism with a tagline  Enchanting Tamil Nadu. Botanical gardens in Ooty and Hogenakal Waterfall on Kaveri River are quite famous. Andhra Pradesh is famous for its religious sites and pilgrimages. Triumala Venkateswara Temple, Birla Mandir, Buddha Statue on Hussain Sagar Lake and Ramappa Temple are some of its most famous tourist spots. castes THE FOUR CASTES OR JATIs in Hinduism are Brahman Kshatriya Vaishya Shudra Favourite Indian Cuisine, Popular Foods of the People #1: Andhra Pradesh  You will love Andhra Pradesh especially if you like spices and chillies. This food is consumed in regions where most people are vegetarians. They had to invent delicious foods to be able to cope with their self imposed condition. eg- Hyderabadi Biryani, Mirchi salan, Ghongura pickle, Korikoora. #2: Goa  Goan cuisine comes from the region of Goa. The actual region is located on the Arabian Sea coast. Its main influences are Hindu, Portuguese and certain contemporary techniques. The cuisine is intensely sea food based. The Kingfish is the symbol of the Goan cuisine.

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