Effective Communication With A Social Worker - 1196 Words

During this course I have learn that one of the crucial components of the social work field is the ability to have effective communication skill. I understand that as a Social Worker we are constantly communicate with clients to gain information, convey critical information and make important decisions. Without effective communication skills, social workers may not be able to obtain or convey that information, thereby causing detrimental effects to clients. Effective communication skill also applies to the relationship you have with your agency supervisor. Effective communication with your supervisor is a key element of your eventual success in the workplace and also with the clients that the agency works with. Understanding this crucial component relationship to the social work field made me want to work on improving and building this skill during my volunteer experience. Prior to starting my volunteer experience I meet with my supervisor to establish my assignments and goals for m y field experience. Furthermore after the meeting I personally assigned myself a conduct for the environment I would be in. We came to the conclusion that my assignment would be to provide both social and emotional support to students in both a small and individual setting. In addition, I would also be participating and engaging with parents during the school monthly parent workshop. The goals I have set for myself was to be able to be able to identify myself as a professional social workerShow MoreRelatedExplain the Role of Effective Communication and Interpersonal Interaction in a Health and Social Care Setting. Skilled Communication Plays a Huge Role in Health and Social Care Such as Psychotherapy, Counselling, Medical and Health Care1117 Words   |  5 PagesExplain the role of effective communication and interpersonal interaction in a health and social care setting. 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Project Coordinator A Program Coordinator - 805 Words

She looked pale, lethargic, and dishevlled sitting in her wheelchair. The pungent odor from her 2 week old gauge was beginning to fill the room. It was clear she was suffering from a medical condition which was being poorly managed. Omar Staples,PA-C removed the bandages and the once fluid filled lesions had burst open and the contents were permeating the gauge causing a decaying odor. This patient was suffering from cutaneous complications due to poor management of her diabetes. Watching Omar talk to her while he was changing her bandages allowed me to observe why patients felt more relaxed and comfortable about sharing their health care concerns with him. I felt a deep sense of empathy for this patient that I had felt before as an undergraduate volunteering with the Adopt-a-grand person program. As project coordinator I worked to alleviate the loneliness among senior citizens in various nursing homes within New Orleans through games, arts crafts, and friendly conversation. 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Evaluative Reasoning Across the Life Span - Myassignmenthelp.Com

Question: Discuss about the Evaluative Reasoning Across the Life Span. Answer: The Kohlberg ethical dilemma is a set of moral issues that question the ethics of an individual blurring the boundary of good and bad or right and wrong. The essay seeks to understand the issue of the dilemma in the modern context with the movie John Q. The movie seeks to analyze and understand the different issues that have been a cause of the moral dilemma among the population through the ages. The ethical dilemma is a situation where a person is made to choose between two categorical imperatives, both of which are equally correct and present a difficulty in choosing a correct option regarding the issue. The ethical choice and the socially accepted choice may not be the same. The dilemma in judging a course of action considering the different scenarios, which are both justified and dependent on subjective opinions, is the object of understanding in this situation (Kahane, et al., 2015). The dilemma and the major issues caused due to it in having conclusive opinions regarding situat ions and their possible responses across different age is the subject of study of Kohlbergs ethical dilemma. The different situations that arise in due time leading to the different subjective opinions and possible solutions towards it. Kohlbergs ethical moral dilemma is evident in a number of movies, where the justification of the negative actions of the protagonist is given in the movie, which makes the audience empathize with him (Weissbourd, Bouffard Jones, 2013). The movie in the given situation, John Q is one such movie where the audience cannot decide the ethics of the actions of protagonist. John Q, is a movie describes and shows the different aspects of human emotion with finesse and ease. The emotion of the father John Quincy Archibald, played by Denzel Washington, is a character who is willing to go to any limits to save the life of his son (Cassavetes, 2002). The film has a number of ethical issues in question, regarding the actions of John in the given situation. The movie starts with an accident where a female rash driver is killed. The later scenes show that the son John Archibald suffers from a major cardiac condition which needs and immediate heart transplant. John is full time factory worker facin g economic issues (Arnold, 2000). When he discovers that the insurance provided by their employer is not supporting the treatment and the surgery of his son, John starts to collect money to get his son enrolled in the organ donors list. Despite all their collective efforts they could not raise more than one third of the necessary amount needed for the surgery. In the situation feeling helpless, John decides to take a drastic measure of holding hostages in the hospital in lieu of his sons treatment. The situation faced by John and his action cannot be justified by an outsiders point of view at this point as all he was doing was to try the best to save the life of his son (Dawson, 2002). The demand of John against the life of 11 hostages held by him is simple, to save the life of his son by putting his name in the organ recipient waiting list. The issues faced by a poor individual and the dramatic situation in which it is resolved shows the moral dilemma that people face in such situa tions. John is a relatively poor person who overworks to meet the need of the family. when he is in need and all his efforts prove futile in saving the life of his son he is agitated at his incapability and decides to take the drastic step of holding the people hostage. It is very tough to judge his dilemma and the justification of his action unless one is in the shoes of John Quincy (Narvaez Lapsley, 2009). He faces a major dilemma of putting the life of a number of people at risk just to save his son. He very well knows that his reputation would be ruined at the end of the day because of the step he has decided to take. The changing scenarios in which the decisions are taken and the humane approach of john in the treatment towards the hostages will help in understanding the perspectives of John. The situation in which John is a very awkward one where it is very tough for an individual to judge his actions on the basis of individual perspectives. The different perspective and consequences of the action of John can be understood in the point of view of John but there are other sides of the story (Edwards Carlo, 2005). The actions of John look justified and in the individualistic perspective of the movie but to do the Kohlberg analysis effectively one needs to see the situation from both the perspectives and present an overview with the situation. When the situation is seen from the perspective of John Quincy, his actions are justified and he has to do whatever he can to save the life of his son. In the movie, it is later shown that he has to load the gun when he is contemplating suicide, which implies that the gun was not loaded during the whole ordeal (Narvaez, 2012). This makes it evident that he did not intend to hurt anyone despite of what others might have thought. It makes his position even more clear in the eyes of the viewers and is shown as protagonist. Moreover, the asking fees of $75000 just to put the name of the child in the organ recipients waiting list garnered sympathy from the audience considering the economic situation John was in. whatever these scenarios showed reinforced the belief of the audience in the innocence of John Quincy. The different situation in which he puts the lives of the hostages at a risk is not one that would put things in favor of him. No matter how humane his appeal was but what he hel d in custody was a hospital and hindered the safety of the people. Moreover, the hospital is a place where people are sick and unhealthy; the situation can push some of them into shock killing a few people. Saving a life is a prime concern that can be understood but the movie raises a number of other important ethical issues while the morality of John Q is judged in the movie. The solitary viewpoint of a single person is show in the movie where the other side is not very evident as shown in the movie Dog Day Afternoon too (Edwards, Carlo, 2005). The dilemma the people or audiences go through is the cinematic genius of the director of the movie who is showing the viewpoint of the protagonist. Since that is the only perspective emphasized in the movie, the people do not understand the overall consequences that the actions of the protagonist. Similarly, in the movie the dog day afternoon the protagonist is shown as the victim of the situation. It is generally common for the audience to empathize with the culprit in this case because most of the people have their own grudges against the norms of the society. The condition, which John Q puts the hospital in, may have gained support of the audience but there are other perspectives to look at. One of the most important issue that is over looked that his son automatically gets the heart of the accident victim who comes in but there is another issue which was over looked. Overlooking the other recipients to give John Quincys son the heart makes the scenario all the more immoral and unethical. Just because he was holding the hospital and a number of patients as hostages does not make the situation justifiable in any sense. Similarly, in the case of the movie Dog Day Afternoon, the family situation of Sonny and the need of his present wife to get operated does not make his actions socially justifiable. There are other ways to deal with situations in the society (Lumet, 1975). There are a number of people in the society, who are facing much harsher situations in life and they choose to fight it. The justification of the situation in movie may seem apt in a number of ways but they do not seem to be convincing. One might not be thinking of the consequences at the moment of taking decisions but the actions may have a lasting effect on the people who were affected. It is taken that both Sonny and John were compassionate and understanding of the hostage situations and the needs (Hart Carlo, 2005). This does not redeem them of their actions completely given that John gets a jail sentence Sonny kills his friend and is jailed for 20 years for his actions. The sympathy is gained from the acceptance of the fact that they faced the consequences of their actions does not make them complete. In both the movies the Kohlbergs moral and ethical dilemma is aggravated and the protagonists are showing doing actions which in a sense redeem them of their ill deeds. One of the situation is in which John is willing to give his own life and loads the gun with a bullet. This shows that he indeed did not seek to hurt anyone in the vicinity as the gun was not loaded and was willing to make the ultimate sacrifice to save the life of his son (Keller Edelstein, 1991). On the other hand sonnys condition in Dog Day afternoon is understood when his wife turns up. The majority of the sympathy that they get from the society in the move is because they are shown treating the hostages with compassion. This seems a very tough predicament for one to decide the justification of their actions since their actions seem right but were against the law and the social norms. The audience empathizes with the protagonist in such situations but the dilemma had not risen if not for their actions. There are a number of movies which show the people in really tough situations but they choose to work hard and fight against the system without breaking it or causing physical or psychological harm to anyone. One of the best movies fitting this example is The Pursuit of Happiness, where Chris Gardener is beaten by the situations and the system a number of times (Muccino, 2006). There are a number of scenarios where he could have gone against the law to fulfill his own needs when all the situations were against him. Given the scenario in the movie, the audience would have accepted his actions as justified too. The story is based on a real story so it cannot be said that it can be only done in movie. The overall scenario of portraying a father fighting for his son makes the situation, emotional and tilts the moral balance in their favor, but it has to be considered th at they were not the best of the alternatives that he adhered to (Pratt, Skoe Arnold, 2004). Alls well that ends well says the phrase but in the case of Dog Day Afternoon, the protagonist losses a lot of thing, most importantly his friend and twenty years of his life. The end of John Quincy shows the protagonist repentant and sentenced for his actions, but the dilemma never ends of who is right. Therefore, it is seen that the movie John Q, show the Kohlbergs moral ethical dilemma in every sense. The movie delves deeper in to the human emotions and the balance between the laws governing the society and the moral reasoning is questioned. The action of John Q when viewed in the perspective of the audience and the overall situation of John is justified. If the situation is viewed in the eyes of the law and the overall scenario where it occurs and the actions may not be justified in any way. It is also notable that the Kohlbergs dilemma of moral ethics can be attributed to all the movies in the action genre, where the protagonist does anything that they desire as a vengeance of something that has hurt them. References Arnold, M. L. (2000). Stage, sequence, and sequels: Changing conceptions of morality, post-Kohlberg.Educational Psychology Review,12(4), 365-383. Cassavetes, N. (2002).John Q (2002). [online] IMDb. Available at: https://www.imdb.com/title/tt0251160/ [Accessed 4 Jan. 2018]. Dawson, T. L. (2000). Moral and evaluative reasoning across the life-span.Journal of Applied Measurement,1(4), 346-371. Dawson, T. L. (2002). New tools, new insights: Kohlberg's moral judgement stages revisited.International Journal of Behavioral Development,26(2), 154-166. Edwards, C. P., Carlo, G. (2005). Moral Development Study in the 21st Century: Introduction to Moral Motivation through the Life Span: Nebraska Symposium on Motivation, volume 51.Faculty Publications, Department of Child, Youth, and Family Studies, 37. Hart, D., Carlo, G. (2005). Moral development in adolescence.Journal of Research on Adolescence,15(3), 223-233. Kahane, G., Everett, J. A., Earp, B. D., Farias, M., Savulescu, J. (2015). Utilitarianjudgments in sacrificial moral dilemmas do not reflect impartial concern for the greater good.Cognition,134, 193-209. Keller, M., Edelstein, W. (1991). The development of socio-moral meaning making: Domains, categories, and perspective-taking.Handbook of moral behavior and development,2, 89-114. Lumet, S. (1975).Dog Day Afternoon (1975). [online] IMDb. Available at: https://www.imdb.com/title/tt0072890/?ref_=nv_sr_1 [Accessed 4 Jan. 2018]. Muccino, G. (2006).The Pursuit of Happyness (2006). [online] IMDb. Available at: https://www.imdb.com/title/tt0454921/?ref_=nv_sr_1 [Accessed 4 Jan. 2018]. Narvaez, D. (Ed.). (2012).Evolution, early experience and human development: From research to practice and policy. Oxford University Press. Narvaez, D., Lapsley, D. K. (2009). Moral identity, moral functioning, and the development of moral character.Psychology of Learning and Motivation,50, 237-274. Pratt, M. W., Skoe, E. E., Arnold, M. L. (2004). Care reasoning development and family socialisation patterns in later adolescence: A longitudinal analysis.International Journal of Behavioral Development,28(2), 139-147. Smith-Osborne, A. (2007). Life span and resiliency theory: A critical review.Advances in social work,8(1), 152-168. Weissbourd, R., Bouffard, S. M., Jones, S. M. (2013). School climate and moral and social development.School Climate Practices for Implementation and Sustainability,30, 1-5.

Online Shopping At The Mall Essay Example For Students

Online Shopping At The Mall Essay Shopping had always been people’s necessity. A few centuries ago, people had to go out on the street or market to purchase goods or food with cash in person as consumers and sellers, but where have all the shoppers gone lately? Because of great improvements in technology, online shopping has arisen. Instead of going to the mall or stores and taking hours to look for what is on the shopping list, online shopping became accessible in the recent generation. In 1979, online shopping was invented by Michael Aldrich who was inspired to connecting a domestic television by telephone line to a real-time transaction that he called teleshopping (Inventor s Story.). Also, online shopping is another way for companies to sell their products to consumers, giving customers the ability to purchase products from their homes. There are definitely reasonable causes why business through online became closer to shoppers. Mainly, the Internet helped in the convenience, easier electronic advertisements, and no physical activity requirements for the shoppers have made online shopping popular. From the customer’s perspective, there is no other reason why they should not use the Internet to shop. Has your mother ever told you she is going grocery shopping and will be back soon, then always takes longer than two hours with a cart full of bags of food that was never on the shopping list? That is because they got caught in store’s marketing strategy. For example, in the front of the stores like Walmart, or Meijer, they display fruits and vegetables so people could walk by and feel fresh and be able to shop longer, or perhaps make them desirable to buy some of it. However, there are no distractions like this on Internet shopping mall. There may be some adve. .urchasing goods, and some other part of people will never stop selling their products. Online shopping will always be a great tool for the faster and easier retail and shop rotation and as internet the and mobile system is still developing and perhaps in the future there might be even greater technology that people in these days cannot even imagine. Pretend the statistic is accurate; the most average Americans are saving 39 percent of time, 36 percent of money, 27percent of gas by driving, and get 15 percent more food from online shopping (A Hartman Group). Although there might risks of no guarantee of consumers’ privacy, online shopping users are increasing every day. This may impact on actual physical stores business, by not appearing to the stores to purchase goods, but also on the other side, online world will only going to get bigger for people’s conveniences.

The Dangers of Over-Reliance on Technology Essay Example

The Dangers of Over-Reliance on Technology Paper Albert Einstein once said, â€Å"It has become appallingly obvious that our technology has exceeded our humanity.† Even though he was referring to atomic bombs, his point is still valid. We are becoming slaves to our own technology and it is happening faster than we can comprehend. Technology has now become an essential part of our lives and we cannot live without it. Our rapidly advancing technology is limiting and crippling our society’s inhabitants. We live in a high tech world, and the more advanced technology becomes, the more it seems to have control over our lives. It is difficult to imagine a world without technology. Technology has a lot of advantages but it has a lot of negative sides and the negative side can have serious and long-term consequences. As of now technology is everywhere, surrounding us, it is stopping us from being ourselves. People who use technology have lack of bonds with others, leading to isolation. Technology also leads to depression, dise ases and a warped sense of reality. Technology causes pollution that leads to natural destruction. Social isolation is characterized by a lack of contact with other people in normal daily living, such as, the workplace, with friends and in social activities. We isolate ourselves by walking around in our own little world. With technology we are creating our own little world and we keep ourselves away from others â€Å"little worlds.† As we can see in the book Feed, by M.T. Anderson, â€Å"Creville Heights was all one big area, instead of each yard with its own sun and season. They must’ve had just one sun for the whole place† (134). In the book Feed, the people had their own bubbles with their own sun and seasons. All this leads to isolation. The use of online social media outlets causes us to meet face-to-face with much less frequency, resulting in a lack of much needed social skills. We can see that today, as Google Glass by Google is the first entry in the wearable We will write a custom essay sample on The Dangers of Over-Reliance on Technology specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on The Dangers of Over-Reliance on Technology specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on The Dangers of Over-Reliance on Technology specifically for you FOR ONLY $16.38 $13.9/page Hire Writer

Extreme conditional value at risk a coherent scenario for risk management The WritePass Journal

Extreme conditional value at risk a coherent scenario for risk management CHAPTER ONE Extreme conditional value at risk a coherent scenario for risk management CHAPTER ONE1. INTRODUCTION1.1.BACKGROUND1.2   RSEARCH PROBLEM1.3   RELEVENCE OF THE STUDY1.4   RESEARCH DESIGNCHAPTER 2: RISK MEASUREMENT AND THE EMPIRICALDISTRIBUTION OF FINANCIAL RETURNS2.1   Risk Measurement in Finance: A Review of Its Origins2.2   Value-at-risk (VaR)2.2.1 Definition and concepts2.2.2 Limitations of VaR2.3   Conditional Value-at-Risk2.4   The Empirical Distribution of Financial Returns2.4.1   The Importance of Being Normal2.4.2 Deviations From NormalityCHAPTER 3: EXTREME VALUE THEORY: A SUITABLE AND ADEQUATE FRAMEWORK?1.3. Extreme Value Theory3.1. The Block of Maxima Method3.2.  Ã‚   The Generalized Extreme Value Distribution3.2.1. Extreme Value-at-Risk3.2.2.   Extreme Conditional Value-at-Risk (ECVaR): An Extreme Coherent Measure of RiskCHAPTER 4: DATA DISCRIPTION.CHAPTER 5: DISCUSION OF EMPIRICAL RESULTSCHAPTER 6: CONCLUSIONS  References Related CHAPTER ONE 1. INTRODUCTION Extreme financial losses that occurred during the 2007-2008 financial crisis reignited questions of whether existing methodologies, which are largely based on the normal distribution, are adequate and suitable for the purpose of risk measurement and management. The major assumptions employed in these frameworks are that financial returns are independently and identically distributed, and follow the normal distribution. However, weaknesses in these methodologies has long been identified in the literature. Firstly, it is now widely accepted that financial returns are not normally distributed; they are asymmetric, skewed, leptokurtic and fat-tailed. Secondly, it is a known fact that financial returns exhibit volatility clustering, thus the assumption of independently distributed is violated. The combined evidence concerning the stylized facts of financial returns necessitates the need for adapting existing methodologies or developing new methodologies that will account for all the stylised facts of financial returns explicitly. In this paper, I discuss two related measures of risk; extreme value-at-risk (EVaR) and extreme conditional value-at-risk (ECVaR). I argue that ECVaR is a better measure of extreme market risk than EVaR utilised by Kabundi and Mwamba (2009) since it is coherent, and captures the effects of extreme markets events. In contrast, even though EVaR captures the effect of extreme market events, it is non-coherent. 1.1.BACKGROUND Markowitz (1952), Roy (1952), Shape (1964), Black and Scholes (1973), and Merton’s (1973) major toolkit in the development of modern portfolio theory (MPT) and the field of financial engineering consisted of means, variance, correlations and covariance of asset returns. In MPT, the variance or equivalently the standard deviation was the panacea measure of risk. A major assumption employed in this theory is that financial asset returns are normally distributed. Under this assumption, extreme market events rarely happen. When they do occur, risk managers can simply treat them as outliers and disregard them when modelling financial asset returns. The assumption of normally distributed asset returns is too simplistic for use in financial modelling of extreme market events. During extreme market activity similar to the 2007-2008 financial crisis, financial returns exhibit behavior that is beyond what the normal distribution can model. Starting with the work of Mandelbrot (1963) there is increasingly more convincing empirical evidence that suggest that asset returns are not normally distributed. They exhibit asymmetric behavior, ‘fat tails’ and high kurtosis than the normal distribution can accommodate. The implication is that extreme negative returns do occur, and are more frequent than predicted by the normal distribution. Therefore, measures of risk based on the normal distribution will underestimate the risk of portfolios and lead to huge financial losses, and potentially insolvencies of financial institutions. To mitigate the effects of inadequate risk capital buffers stemming from underestimation of risk by normality-based financial modelling, risk measures such as EVaR that go beyond the assumption of normally distributed returns have been developed. However, EVaR is non-coherent just like VaR from which it is developed. The implication is that, even though it captures the effects of extreme mar ket events, it is not a good measure of risk since it does not reflect diversification – a contradiction to one of the cornerstone of portfolio theory. ECVaR naturally overcomes these problems since it coherent and can capture extreme market events. 1.2   RSEARCH PROBLEM The purpose of this paper is to develop extreme conditional value-at-risk (ECVaR), and propose it as a better measure of risk than EVaR under conditions of extreme market activity with financial returns that exhibit volatility clustering, and are not normally distributed. Kabundi and Mwamba (2009) have proposed EVaR as a better measure of extreme risk than the widely used VaR, however, it is non-coherent. ECVaR is coherent, and captures the effect of extreme market activity, thus it is more suited to model extreme losses during market turmoil, and reflects diversification, which is an important requirement for any risk measure in portfolio theory. 1.3   RELEVENCE OF THE STUDY The assumption that financial asset returns are normally distributed understates the possibility of infrequent extreme events whose impact is more detrimental than that of events that are more frequent. Use of VaR and CVaR underestimate the riskiness of assets and portfolios, and eventually lead to huge losses and bankruptcies during times of extreme market activity. There are many adverse effects of using the normal distribution in the measurement of financial risk, the most visible being the loss of money due to underestimating risk. During the global financial crisis, a number of banks and non-financial institutions suffered huge financial losses; some went bankrupt and failed, partly because of inadequate capital allocation stemming from underestimation of risk by models that assumed normally distributed returns. Measures of risk that do not assume normality of financial returns have been developed. One such measure is EVaR (Kabundi and Mwamba (2009)). EVaR captures the effect of extreme market events, however it is not coherent. As a result, EVaR is not a good measure of risk since it does not reflect diversification. In financial markets characterised by multiple sources of risk and extreme market volatility, it is important to have a risk measure that is coherent and can capture the effect of extreme market activity. ECVaR   is advocated to fulfils this role of ensuring extreme market risk while conforming to portfolio theory’s wisdom of diversification. 1.4   RESEARCH DESIGN Chapter 2 will present a literature review of risk measurement methodologies currently used by financial institutions, in particular, VaR and CVaR. I also discuss the strengths and weaknesses of these measures. Another risk measure not widely known thus far is the EVaR. We discuss EVaR as an advancement in risk measurement methodologies. I advocate that EVaR is not a good measure of risk since it is non-coherent. This leads to the next chapter, which presents ECVaR as a better risk measure that is coherent and can capture extreme market events. Chapter 3 will be concerned with extreme conditional value-at-risk (ECVaR) as a convenient modelling framework that naturally overcomes the normality assumption of asset returns in the modelling of extreme market events. This is followed with a comparative analysis of EVaR and ECVaR using financial data covering both the pre-financial crisis and the financial crisis periods. Chapter 4 will be concerned with data sources, preliminary data description, and the estimation of EVaR, and ECVaR. Chapter 5 will discuss the empirical results and the implication for risk measurement. Finally, chapter 6 will give concussions and highlight the directions for future research. CHAPTER 2: RISK MEASUREMENT AND THE EMPIRICAL DISTRIBUTION OF FINANCIAL RETURNS 2.1   Risk Measurement in Finance: A Review of Its Origins The concept of risk has been known for many years before Markowitz’s Portfolio Theory (MPT). Bernoulli (1738) solved the St. Petersburg paradox and derived fundamental insights of risk-averse behavior and the benefits of diversification.   In his formulation of expected utility theory, Bernoulli did not define risk explicitly; however, he inferred it from the shape of the utility function (Bulter et al. (2005:134); Brancinger Weber, (1997: 236)). Irving Fisher (1906) suggested the use of variance to measure economic risk. Von Neumann and Morgenstern (1947) used expected utility theory in the analysis of games and consequently deduced many of the modern understanding of decision making under risk or uncertainty.   Therefore, contrary to popular belief, the concept of risk has been known well before MPT. Even though the concept of risk was known before MPT, Markowitz (1952) first provided a systematic algorithm to measure risk using the variance in the formulation of the mean-variance model for which he won the Nobel Prize in 1990. The development of the mean-variance model inspired research in decision making under risk and the development of risk measures. The study of risk and decision making under uncertainty (which is treated the same as risk in most cases) stretch across disciplines. In decision science and psychology, Coombs and Pruitt (1960), Pruitt (1962), Coombs (1964), Coombs and Meyer (1969), and Coombs and Huang (1970a, 1970b) studied the perception of gambles and how their preference is affected by their perceived risk. In economics, finance and measurement theory, Markowitz (1952, 1959), Tobin (1958), Pratt (1964), Pollatsek Tversky (1970), Luce (1980) and others investigate portfolio selection and the measurement of risk of those portfolios, and gambles in general. T heir collective work produces a number of risk measures that vary in how they rank the riskiness of options, portfolios, or gambles. Though the risk measures vary, Pollatsek and Tversky (1970: 541) recognises that they share the following:   (1) Risk is regarded as a property of choosing among options. (2) Options can be meaningfully ordered according to their riskiness. (3) As suggested by Irving Fisher in 1906, the risk of an option is somehow related to the variance or dispersion in its outcomes. In addition to these basic properties, Markowitz regards risk as a ‘bad’, implying something that is undesirable. Since Markowitz (1952), many risk measures such as the semi-variance, absolute deviation, and the lower semi-variance etc. (see Brachinger and Weber, (1997)) were developed, however, the variance continued to dominate empirical finance. It was in the 1990s that a new measure, VaR was popularised and became industry standard as a risk measure. I present this ris k measure in the next section. 2.2   Value-at-risk (VaR) 2.2.1 Definition and concepts Besides these basic ideas concerning risk measures, there is no universally accepted definition of risk (Pollatsek and Tversky, 1970:541); as a result, risk measures continue to be developed. J.P Morgan Reuters (1996) pioneered a major breakthrough in the advancement of risk measurement with the use of value-at-risk (VaR), and the subsequent Basel committee recommendation that banks could use it for their internal risk management. VaR is concerned with measuring the risk of a financial position due to the uncertainty regarding the future levels of interest rates, stock prices, commodity prices, and exchange rates. The risk resulting in the movement of these market factors is called market risk. VaR is the expected maximum loss of a financial position with a given level of confidence over a specified horizon. VaR provides answers to question: what is the maximum loss that I can lose over, say the next ten days with 99 percent confidence? Put differently, what is the maximum loss that will be exceeded only one percent of the times in the next ten day? I illustrate the computation of VaR using one of the methods that is available, namely parametric VaR. I denote by the rate of return and by the portfolio value at time. Then is given by (1) The actual loss (the negative of the profit, which is) is given by (2) When is normally distributed (as is normally assumed), the variable has a standard normal distribution with mean of and standard deviation of. We can calculate VaR from the following equation: (3) where implies a confidence level. If we assume a 99% confidence level, we have (4) In   we have -2.33 as our VaR at 99% confidence level, and we will exceed this VaR only 1% of the times. From (4), it can be shown that the 99% confidence VaR is given byVaR (5)Generalising from (5), we can state the quantile VaR of the distribution as follows (6)VaR is an intuitive measure of risk that can be easily implemented. This is evident in its wide use in the industry. However, is it an optimal measure? The next section addresses the limitations of VaR. 2.2.2 Limitations of VaR Artzner et al. (1997,1999) developed a set of axioms that if satisfied by a risk measure, then that risk measure is ‘coherent’. The implication of coherent measures of risk is that â€Å"it is not possible to assign a function for measuring risk unless it satisfies these axioms† (Mitra, 2009:8). Risk measures that satisfy these axioms can be considered universal and optimal since they are founded on the same mathematical axioms that are generally accepted. Artzner et al. (1997, 1999) put forward the first axioms of risk measures, and any risk measure that satisfies them is a coherent measure of risk. Letting be a risk measure defined on two portfolios and. Then, the risk measure is coherent if it satisfies the following axioms: (1)  Ã‚   Monotonicity:   if then We interpret the monotonicity axiom to mean that higher losses are associated with higher risk. (2)  Ã‚   Homogeneity:   Ã‚   for; Assuming that there is no liquidity risk, the homogeneity axiom mean that risk is not a function of the quantity of a stock purchased, therefore we cannot reduce or increase risk by investing different amounts in the same stock. (3)  Ã‚   Translation invariance: , where is a riskless security; This means that investing in a riskless asset does not increase risk with certainty. (4)  Ã‚   Sub-additivity:   Possibly the most important axiom, sub-additivity insures that a risk measure reflects diversification – the combined risk of two portfolios is less than the sum of the risks of individual portfolios. VaR does not satisfy the most important axiom of sub-additivity, thus it is non-coherent. More so, VaR tells us what we can expect to lose if an extreme event does not occur, thus it does not tell us the extend of losses we can incur if a â€Å"tail† event occurs. VaR is therefore not optimal measure of risk. The non-coherence, and therefor non-optimality of VaR as a measuring of risk led to the development of conditional value-at-risk (CVaR) by Artzner et al. (1997, 1999), and Uryasev and Rockafeller (1999). I discus CVaR in the next section. 2.3   Conditional Value-at-Risk CVaR is also known as â€Å"Expected Shortfall† (ES),     Ã¢â‚¬Å"Tail VaR†, or â€Å"Tail conditional expectation†, and it measures risk beyond VaR. Yamai and Yoshiba (2002) define CVaR as the conditional expectation of losses given that the losses exceed VaR. Mathematically, CVaR is given by the following: (7) CVaR offers more insights concerning risk that VaR in that it tells us what we can expect to lose if the losses exceed VaR. Unfortunately, the finance industry has been slow in adopting CVaR as its preferred risk measure. This is besides the fact that â€Å"the actuarial/insurance community has tended to pick up on developments in financial risk management much more quickly than financial risk managers have picked up on developments in actuarial science† (Dowd and Black (2006:194)). Hopefully, the effects of the financial crisis will change this observation. In much of the applications of VaR and CVaR, returns have been assumed to be normally distributed. However, it is widely accepted that returns are not normally distributed. The implication is that, VaR and CVaR as currently used in finance will not capture extreme losses. This will lead to underestimation of risk and inadequate capital allocation across business units. In times of market stress when extra capital is required, it will be inadequate. This may lead to the insolvency of financial institutions. Methodologies that can capture extreme events are therefore needed. In the next section, I discuss the empirical evidence on financial returns, and thereafter discuss extreme value theory (EVT) as a suitable framework of modelling extreme losses. 2.4   The Empirical Distribution of Financial Returns Back in 1947, Geary wrote, â€Å"Normality is a myth; there never was, and never will be a normal distribution† (as cited by Krishnaiah (1980: 279). Today this remark is supported by a voluminous amount of empirical evidence against normally distributed returns; nevertheless, normality continues to be the workhorse of empirical finance. If the normality assumption fails to pass empirical tests, why are practitioners so obsessed with the bell curve? Could their obsession be justified? To uncover some of the possible responses to these questions, let us first look at the importance of being normal, and then look at the dangers of incorrectly assuming normality. 2.4.1   The Importance of Being Normal The normal distribution is the widely used distribution in statistical analysis in all fields that utilises statistics in explaining phenomenon. The normal distribution can be assumed for a population, and it gives a rich set of mathematical results (Mardia, 1980: 279). In other words, the mathematical representations are tractable, and are easy to implement. The populations can simply be explained by its mean and variance when the normal distribution is assumed. The panacea advantage is that the modelling process under normality assumption is very simple. In fields that deal with natural phenomenon, such as physics and geology, the normal distribution has unequivocally succeeded in explaining the variables of interest. The same cannot be said in the finance field. The normal probability distribution has been subject to rigorous empirical rejection. A number of stylized facts of asset returns, statistical tests of normality and the occurrence of extreme negative returns disputes the normal distribution as the underlying data generating process for asset returns. We briefly discuss these empirical findings next. 2.4.2 Deviations From Normality Ever since Mandelbrot (1963), Fama (1963), Fama (1965) among others, it is a known fact that asset returns are not normally distributed. The combined empirical evidence since the 1960s points out the following stylized facts of asset returns: (1)  Ã‚   Volatility clustering: periods of high volatility tend to be followed by periods of high volatility, and period of low volatility tend to be followed by low volatility. (2)  Ã‚   Autoregressive price changes: A price change depends on price changes in the past period. (3)  Ã‚   Skewness: Positive prices changes and negative price changes are not of the same magnitude. (4)  Ã‚   Fat-tails: The probabilities of extreme negative (positive) returns are much larger than predicted by the normal distribution. (5)  Ã‚   Time-varying tail thickness: More extreme losses occur during turbulent market activity than during normal market activity. (6)  Ã‚   Frequency dependent fat-tails: high frequency data tends to be more fat-tailed than low frequency data. In addition to these stylized facts of asset returns, extreme events of 1974 Germany banking crisis, 1978 banking crisis in Spain, 1990s Japanese banking crisis, September 2001, and the 2007-2008 US experience ( BIS, 2004) could not have happened under the normal distribution. Alternatively, we could just have treated them as outliers and disregarded them; however, experience has shown that even those who are obsessed with the Gaussian distribution could not ignore the detrimental effects of the 2007-2008 global financial crisis. With these empirical facts known to the quantitative finance community, what is the motivation for the continued use of the normality assumption? It could be possible that those that stick with the normality assumption know only how to deal with normally distributed data. It is their hammer; everything that comes their way seems like a nail! As Esch (2010) notes, for those that do have other tools to deal with non-normal data, they continue to use the normal distribution on the grounds of parsimony. However, â€Å"representativity should not be sacrificed for simplicity† (Fabozzi et al., 2011:4). Better modelling frameworks to deal with extreme values that are characteristic of departures from normality have been developed. Extreme value theory is one such methodology that has enjoyed success in other fields outside finance, and has been used to model financial losses with success. In the next chapter, I present extreme value-based methodologies as a practical and better methodology to overcome non-normality in asset returns. CHAPTER 3: EXTREME VALUE THEORY: A SUITABLE AND ADEQUATE FRAMEWORK? 1.3. Extreme Value Theory Extreme value theory was developed to model extreme natural phenomena such as floods, extreme winds, and temperature, and is well established in fields such as engineering, insurance, and climatology. It provides a convenient way to model the tails of distributions that capture non-normal activities. Since it concentrates on the tails of distributions, it has been adopted to model asset returns in time of extreme market activity (see Embrechts et al. (1997); McNeil and Frey (2000); Danielsson and de Vries (2000). Gilli and Kellezi (2003) points out two related ways of modelling extreme events. The first way describes the maximum loss through a limit distribution known as the generalised extreme value distribution (GED), which is a family of asymptotic distributions that describe normalised maxima or minima.   The second way provides asymptotic distribution that describes the limit distribution of scaled excesses over high thresholds, and is known as the generalised Pareto distribution (GPD). The two limit distributions results into two approaches of EVT-based modelling the block of maxima method and the peaks over threshold method respectively[2]. 3.1. The Block of Maxima Method Let us consider independent and identically distributed (i.i.d) random variable   with common distribution function â„ ±. Let be the maximum of the first random variables. Also, let us suppose is the upper end of. For, the corresponding results for the minima can be obtained from the following identity (8) almost surely converges to whether it is finite or infinite since, Following Embrechts et al. (1997), and Shanbhang and Rao (2003), the limit theory finds norming constants and a non-degenerate distribution function in such a way that the distribution function of a normalized version of converges to as follows;, as (9) is an extreme value distribution function, and â„ ± is the domain of attraction of, (written as), if equation (2) holds for suitable values of and. It can also be said that the two extreme value distribution functions and belong in the same family if for some   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   and all. Fisher and Tippett (1928), De Haan (1970, 1976), Weissman (1978), and Embrechts et al. (1997) show that the limit distribution function belongs to one of the following three density functions for some. (10) (11) (12) Any extreme value distribution can be classified as one of the three types in (10), (11) and (12).   and   are the standard extreme value distribution and the corresponding random variables are called standard extreme random variables. For alternative characterization of the three distributions, see Nagaraja (1988), and Khan and Beg (1987). 3.2.  Ã‚   The Generalized Extreme Value Distribution The three distribution functions given in (10), (11) and (12) above can be combined into one three-parameter distribution called the generalised extreme value distribution (GEV) given by,, with (13) We denote the GEV by, and the values andgive rise to the three distribution functions in (3). In equation (4) above, and represent the location parameter, the scale parameter, and the tail-shape parameter respectively. corresponds to the Frechet, and distributioncorresponds to the Weibull distribution. The case where reduces to the Gumbel distribution. To obtain the estimates of we use the maximum likelihood method, following Kabundi and Mwamba (2009). To start with, we fit the sample of maximum losses to a GEV. Thereafter, we use the maximum likelihood method to estimate the parameters of the GEV from the logarithmic form of the likely function given by; (14) To obtain the estimates of we take partial derivatives of equation (14) with respect to and, and equating them to zero. 3.2.1. Extreme Value-at-Risk The EVaR defined as the maximum likelihood   quantile estimator of, is by definition given by (15)   The quantity is the quantile of, and I denote it as the alpha percept VaR specified as follows following Kabundi and Mwamba (2009), and Embrech et al. (1997): (16) Even though EVaR captures extreme losses, by extension from VaR it is non-coherent. As such, it cannot be used for the purpose of portfolio optimization since it does not reflect diversification. To overcome this problem, In the next section, I extend CVaR to ECVaR so as to capture extreme losses coherently. 3.2.2.   Extreme Conditional Value-at-Risk (ECVaR): An Extreme Coherent Measure of Risk I extend ECVaR from EVaR in a similar manner that I used to extend CVaR from VaR. ECVaR can therefore be expressed as follows: (17) In the following chapter, we describe the data and its sources. CHAPTER 4: DATA DISCRIPTION. I will use stock market indexes of five advanced economies comprising that of the United States, Japan, Germany, France, and United Kingdom, and five emerging economies comprising Brazil, Russia, India, China, and South Africa. Possible sources of data that will be used are I-net Bride, Bloomberg, and individual country central banks. CHAPTER 5: DISCUSION OF EMPIRICAL RESULTS In this chapter, I will discuss the empirical results. Specifically, the adequacy of ECVaR will be discussed relative to that of EVaR. Implications for risk measurement will also be discussed in this chapter. CHAPTER 6: CONCLUSIONS This chapter will give concluding remarks, and directions for future research.   References [1] Markowitz, H.M.: 1952, Portfolio selection, Journal of Finance 7 (1952), 77-91 2 Roy, A.D.: 1952, Safety First and the Holding of Assets. Econometrica, vol. 20 no 3 p 431-449. 3 Shape, W.F.: 1964, Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, Vol. 19 No 3 p 425-442. 4 Black, F., and Scholes, M.: 1973, The Pricing of Options and Corporate Liabilities, Journal of Political Economy, vol. 18 () 637-59. 5 Merton, R. C.: 1973, The Theory of Rational Option Pricing.   Bell Journal of Economics and Management Science, Spring. 6 Artzner, Ph., F. Delbaen, J.-M. Eber, And D. Heath .: 1997, Thinking Coherently, Risk 10 (11) 68–71. 7 Artzner, Ph., Delbaen, F., Eber, J-M., And Heath , D.: 1999, Thinking Coherently. 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Application of specific energy and momentum function Coursework

Application of specific energy and momentum function - Coursework Example Conversely, the increase, in short, smooth step with the change in y within the channel and with the given upstream depth and corresponding discharge, y3 is increased. Y3 is increased due to the expansion and energy loss. A hydraulic jump is utilized for energy dissipation that occurs when the flows transitions from the supercritical to corresponding subcritical mainly due to the spillway and the steep slope to the mild slope. The depth of water downstream from the jump and the location of the jump are computed using the conservation of energy equation (Kiselev, Fomin & Vorozhtsov, 1999). It is expected from y1 that the depth of water to escalate as the specific energy of the prevailing reduces slowly. Moreover, the alternate depths at which the specific energies ought to be identical. Nevertheless, the values collected does not depict that as the underlying values were not adequate to produce the correct and expected graph thus the association was not represented as anticipated. The prevailing graphs derived from the depths of the flow, and corresponding specific energy at the section depicts that the depth escalates as the time elapses linearly with the specific energy indicating that the two underlying variables are linearly associated. The percentage relative head loss for the underlying theoretical outcomes is relatively higher than the corresponding practical percentage relative to the head loss. The difference is due to the depth of water subsequent to the hydraulic jump that was higher than that of the underlying experimental values. The energy is lost because of the turbulent flow implying that the real water depth is relatively lower than prevailing theoretical computations. The trend line depicts the positive correlation amidst the escalation of the Froude number and corresponding y3/y1 values. The experiment was undertaken under the controlled situations in order to