Internet Censorship Issue Essay Example for Free

Internet Censorship Issue Essay We are exposed to various dangers of the known and unknown kind in this physical world we live in. As such, we are always on the lookout for warning and danger signals regarding problems we may encounter. Our protection from these dangers come in the form of laws, law enforcers, and guidelines that help provide us with a semblance of security and protection. We are protected from unnecessary influences of the mass media by censorship rules designed to temper and minimize the effects of violence, pornography, and other strong yet bad influences on our being. We do not have those safety nets in place, although it is really a necessary precaution, when we use the Internet. This is why I am in favor or Internet filtering in schools and all public libraries. My parents and I are well informed about the dangers posed by unsupervised Internet usage. That is why my parents installed an Internet filtering system in our computer at home. They love me dearly and do not want any harm to come my way nor do they want me to be exposed to anything that could pose a potential danger to my person. But, they cannot spend all their time watching me as I use the Internet for my various activities from day to day. They also do not expect themselves or me to know which disguised websites would be detrimental to myself since all the Internet sites are designed to entice unknowing visitors into their sites. This is why they installed the Internet filter. To act as a back up guide in order for me to informed decisions about which websites will be good to visit and those not fit for people of my age. As such, they put blind faith and trust into the school system hoping that the system also does their part in protecting me from these potential dangers. The school accomplishes this task by installing Internet Filtering programs in the library computers. These programs are designed to aid the school in making sure that students library Internet privileges are not abused, nor used for illegal activities such as online gambling and accessing porn sites. The Internet use remains indirectly supervised for the students and the schools own protection. The Internet has become far more influential than the television in educating the children and allowing them to pursue their interests. It is a learning tool that can help develop a students interest and skills just as fast as the wrong Internet exposure can destroy that very same promising student. This is why Internet filtering should become a standard in all school and public library computers across the country.

The Eight Principles Of Total Quality Management Information Technology Essay

The Eight Principles Of Total Quality Management Information Technology Essay Total Quality Management is a process that ensures continuous improvement for an organisations future. It is a management system with customer satisfaction as a continual goal. It is TQMs goal to integrate a sense of quality into the culture of how an organisation is run. It hopes to merge different internal branches of an organisation (finance, Communications, RD etc.) and link them all with quality being their main goal, with a view to continual customer focus. This system of management contains 8 principles which are vital in implementing this strategy into an organisations culture. These principles combined with effective leadership should result in a company doing The Right Things Right, First Time. TQM stems from the principle that It is easier to control systems than it is people. Companies depend on their customers to keep them in business. It is essential that a company can keep their customers happy by ensuring that the products received are fit for their intended use. It is also very valuable if a company can foresee future customer needs to ensure they keep the customer base in the future. Here the company can ensure that all its new objectives are customer satisfaction based and can later apply measuring techniques to see if their approach is effective (customer surveys etc.) Leadership Total quality should be adopted into the culture of the organization so proper leadership should create an environment where this is possible. The objectives should be clearly laid out with an emphasis on customer satisfaction. A leader should ensure that all employees are fully aware of their importance to the organisation and should eliminate fear and promote trust. Involvement of People Every person within the organisation must be utilized for their specific skills so everyone is contributing to the organizations goals. This helps create a sense of unity and purpose and leads to a better working environment. This leads to people being accountable for their work and serves as a baseline for quality. It involves people sharing ideas and practices and leads to better trouble shooting. Process Approach The objectives should be clearly outlined and all personnel should be aware of hoe the process will be managed and undertaking. This removes the waste of resources and ensures all efforts are placed on defined essential tasks. This helps increase the rate of work and processes are finished faster. If the process has been properly defined and it has to be repeated, the exact same system can be used as it has been documented and this will further increase efficacy of the work in the future. Systems Approach to Management If you can define interrelated systems, they can be correlated and run under one management guise. Running these various operations under one system leads to more effective work and allows the system to be monitored more easily and have data compiled on the systems efficacy. Through this monitoring continual improvement can be scored and documented. By aligning different departments within an organisation, more focus can be placed on key goals and confidence in the work will be widespread. This can lead to greater results faster. Continual Improvement This is to be a permanent goal for every process undertaken within the organisation. By training staff to use the various continual improvement tools available, a company can leap on an improvement opportunity before others. This could possible open up a new customer market that was previously unavailable. Factual Approach to Decision Making Decisions should be made based on analysis of sound data and intensively researched information. This removes poor decision making from an organisation and sets a baseline for decision making in the future. This can also help the ability to demonstrate how effective past decisions were by checking factual data. The factual data can help access the outcome of the decision and help removes decision making based on intuition alone. Mutually Beneficial Supplier relationship A relationship based on mutual benefits is very healthy and ensures that both parties have each other interests in focus. This will help create value for both companies if some parameters are set correctly. Open communication must be maintained and key objectives and future plans must be known to both parties. If both parties can pool their resources and both have a strong view towards customer satisfaction then waste of resources can be minimised and activities can be improved on both sides. Implementing TQM: TQM is a complex management system that will require a lot of input from all people involved in the company. However, the most important initiating step is to get full commitment from the companys senior management. Without this a proper plan cannot be devised and TQM will not work. A quality team or quality council must be set up among senior managers. Here ideas can be exchanged and improvements to specific parts of the company devised. Here it will also be decided who is to implement certain changes and a system of quality reporting must also be set up. There should be a quality improvement team and also a quality planning team set up. These teams should contain people who represent all areas of the organisation and have a high standard of knowledge of their individual departments. It is essential that the individual department are not run separately, but rather as one large system to ensure full communication remains and key objectives can be achieved faster. Once senior management have become involved the next step is to make middle management aware of the transition. This will involve meeting between middle management and the personnel who report to them. Here all staff will be educated about the forthcoming move to TQM and a series of plans will be set in motion. Here the staff should constantly retrain and each department should develop new experts to keep a focus on continual improvement. A mission statement of the organisations quality policy is a great way to start. From here a series of plans and a systematic approach can be developed to convert the companys operations to one of total quality. This change is very serious and without full commitment and proper planning can fail easily. The first thing a company must do is assess their current state. Here a management audit is a valuable tool and can easily identify the companys health. If the company is in poor health (quality standards low, reactive decisions, and poor managerial skill) then TQM will be near impossible to implement. However, if a company can agree that its current level of management, organisational culture and work ethos are suitable to TQM, then the process can develop from here. Benefits of TQM: Once an organisation has been converted to a system of total quality management, the organisation may be able for ISO 9000 accreditation. This is an internationally recognized standard of quality that has a major impact on how your organisation itself and the process that are undertaken are viewed by the public/possible customers. Having this accreditation will also greatly benefit the organisation from a financial point of view. Getting insurance for large companies can a massive financial burden and being ISO 9000 approved means you are more likely to be insured. This will help protect the organisation from possible liable action. The main benefits internally are a new streamlined system for operations. Increased workmanship due to work ownership/accountability and this leads to constant quality. Increased readiness for the future market and a removal from a reactive decision making process and overall improved stability within the market and increased staying power. Disadvantages of TQM: A main concern of implementing total quality management is the initial set up cost. Here employees will have to attend training that will take away from their usual duties and cause a dip in productivity. Due to all the changes implementing TQM will cause, there is a school of thought that thinks employees will doubt the security of their positions. This may lead them to becoming resistant to change and as a result, slow down the implementation stages and the efficacy of TQM. The results which are desired from TQMs implementation may also take years to see, this can lead the employees feeling that their input was of little use and the project was a waste of time. TQM can also tie a business to a certain set of plans that may span years. This can lead the company down an inflexible route when it comes to future prospect and developments. Instead of the company continually focusing on the goals TQM was supposed to tackle, the main focus in put on finishing the implementation stage. As a result, the company ends up with a major organisation change but the highlighted problems still exist and more time will have to be delegated to solving these at a later time. This again all leads to a drop in current productivity and the business retains its previous efficacy with no notable improvements being recognisable.

Strength of the electromagnet

Strength of the electromagnet Aim: To investigate how different factors affect the strength of the electromagnet. Introduction: There are totally two factors that I am going to investigate in this experiment: m F1: How the number of coils affects the strength of the electromagnet. m F2: How a change in current affects the strength of an electromagnet. Hypothesis: m F1: I think as the number of coils increase the strength of the electromagnet would also increase. m F2: I think as the current increases the strength of the electromagnet would also increase. Variables: m F1: * Independent: Number of coils. * Dependent: Distance at which compass needle deflects.(+/-0.05 cm) Â · Constants: Current, diameter of the wire, temperature, same iron core. m F2: * Independent: Current (+/-0.01amps) Â · Dependent: Distance at which compass needle deflects.(+/-0.05 cm) Â · Constants: Number of coils, diameter of the wire, temperature, same iron core. Apparatus: * Power Supply * Ammeter (Â ±0.01amps) * Electromagnets with different number of coils * Plastic Ruler (Â ±0.05cm) * Plotting Compass * Crocodile clip wires * Rheostat Procedure: Â · Using crocodile clips connect the circuit in the following way: Â · F1: Â § Take an iron nail and with the help of a nichrome wire, coil it around the nail 5 times. Â § Switch the circuit on and wait for a minute. Â § Note down the constant current. Â § Take the plotting compass and steadily place it close to the electromagnet and slide it away in a straight line till the needle deflects slightly to one side from its vertically straight position. Â § Place a ruler from the north pole of the electromagnet and note the distance where this deflection occurs. This where the magnetic field lines would approximately end, and hence more the distance more the field lines and greater the strength. Â § Repeat the steps above but with 10, 15, 20, 25, 30 and 35 coils. Â § For the same number of coils measure the distance of deflection 2 times for a second trial. Â · F2: Â § Using the same circuit now adjust the variable resistor so that u have a current of 0.5amps flowing through the circuit. Â § Make the constant number of coils to be 10 coils. Â § Take the plotting compass and steadily place it close to the electromagnet and slide it away in a straight line till the needle deflects slightly to one side from its vertically straight position. Â § Place a ruler from the north pole of the electromagnet and note the distance where this deflection occurs. This where the magnetic field lines would approximately end, and hence more the distance more the field lines and greater the strength. Â § Repeat the steps above but with 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 and 5.5 amps. Â § For the same current record the distance two times for a second trial. Raw Data Table F1: Effect of the number of coils on the strength of the electromagnet. Number of Coils of electromagnet Distance of deflection from North Pole of the magnet(+/-0.01cm) Trial 1 Trial 2 5 1.7 1.9 10 4.9 5.1 15 7.6 7.5 20 9.5 9.5 25 12.3 12.1 30 14.8 15 35 16.9 17.2 Constant Current 0.75 A F2: How changing current affects the distance at which the needle deflects. Current (+/-0.01A) Distance from North Pole of the magnet(+/-0.05cm) Trial 1 Trial 2 0.5 1.3 1.2 1.0 2.9 3.0 1.5 4.6 4.6 2.0 6.2 6.3 2.5 7.9 8.0 3.0 9.3 9.5 3.5 10.7 10.8 4.0 11.6 11.8 4.5 12.1 12.3 5.0 12.6 12.9 5.5 12.8 12.8 Constant No. of Coils 10 Processed Data Table: The only processing that can be done in this experiment is to find the average distance for the investigations for both the trials and hence making it easier to make the graph. F1: Effect of the number of coils on the strength of the electromagnet. Number of Coils of electromagnet Average Distance of deflection from North Pole of the magnet(+/-0.05cm) 5 1.8 10 5 15 7.55 20 9.5 25 12.2 30 14.9 35 17.05 Constant Current 0.75 A F2: How changing current affects the distance at which the needle deflects. Current (+/-0.01A) Average Distance from North Pole of the magnet (+/-0.05cm) 0.5 1.25 1.0 2.95 1.5 4.60 2.0 6.25 2.5 7.95 3.0 9.40 3.5 10.75 4.0 11.70 4.5 12.20 5.0 12.75 5.5 12.80 Constant number of coils 10 Now I will plot the graphs of both these averages. Graph Analysis: (Graph at the End) F1: As you can see the graph is proportionate. As the number of coils increases the strength of the electromagnet also increases. There is a positive co-relation and this can be proved by theory as well. As the number of coils increase, the magnetic field of each coil also increases and hence a larger magnetic field would cause the electromagnet to be stronger. There is only one anomaly in our results at 20 coils and this can be neglected as it is a very minor anomaly. F2: In this graph also we notice that there is a positive co-relationship and hence the current is proportionate to the strength of the magnet. As current in a circuit increases the strength of the magnet would also increase as the coil is provided with more charge and hence the field lines would be spread over a larger area and the strength would be larger. But in this graph after 4.0 amps the graph is no longer proportionate. This can be due to the large amount of heat generated in the wires causing more resistance and the value of current would have changed and hence the curve in the graph. Conclusion: Our hypothesis for both the factors was proven right by our graphs. Strength of an electromagnet is proportionate to the current and the number of coils in the solenoid. As the number of coils increase the area of the magnetic field lines also increases hence a stronger electromagnet is produced. It is the same for an increase in current. But after a certain current it becomes tough to maintain other constant factors like temperature which might cause inaccurate results. Evaluation: Â · We could have taken more trials to get better results. Â · We could have used an insulated wire so that the wire wont heat up so fast and it wouldnt have changed our results. Â · The current wasnt always exact. It kept fluctuating hence it may have given inaccurate readings. Â · The exact distance where the needle turned would be very tough to find out as it turns over a range of distance hence that may have given inaccurate results. Fair Test: * The distance between the coils was kept the same in all the trials. * For the first experiment we kept the current constant so that we can fairly compare the results. * For the second experiment we kept the number of coils the same so that we can fairly compare the results. * And for both the experiments we kept the same material of the core and the wire. Safe Test: Â · As it was dealing with electricity we made sure we did not touch any open ends to prevent ourselves from getting a shock. Â · Also we did not touch the wire right after the experiment was over as it may have been quite hot.

Free College Admissions Essays: FBI Agent :: College Admissions Essays

FBI Agent In all respect I've always had a fascination with becoming an F.B.I. agent. As my interest grow during my educational progression, I'm learning how my personal attributes with help me in my given field. First of is the size of my physical build, which I believe will help me with any possible altercation I my encounter. My positive attitude toward life will also be helpful. I also have a good decision making skills, with out letting let and kind of prejudice entering my mind. The reason I am so positive about this is I've had such a variety of friends. I've had friends of different races, ethnic background, and religious beliefs. I've also have a vary strong emotional barrier due to all the tragedy I've gone though in my life. I've lost three of my closet friends in the last four years. Through all those years I was the person who was strong to help my entire friends threw our losses. When I get into the field all the knowledge I've acquired in my education will in all intent be usel ess. How do I know this you ask? Several people I know in several justice fields have told me that the real education comes in the field. Their have several movies such as "The Siege", Silence of the Lambs" witch have inspired me to follow in this possible life style. The T.V. show cops is the most instamental to me, due to the way they always explain how to dissolve every encounter, and what would happen to the perpetrator. These movies and T.V. shows give a positive outlook on my possible life in handling these issues. I would not leave out the possibility of becoming a scout leader. The reason I think I could handle this job is because I've always had an attraction to teaching children. I have been babysitting children for seven years, for my next-door neighbors and my aunt. Any other person besides their parents or me could always never control the three boys next door. Then there are my two cousins who were both girls. I always loved watching children; I just get along with the m so well. I believe I could make a difference in their lives. Free College Admissions Essays: FBI Agent :: College Admissions Essays FBI Agent In all respect I've always had a fascination with becoming an F.B.I. agent. As my interest grow during my educational progression, I'm learning how my personal attributes with help me in my given field. First of is the size of my physical build, which I believe will help me with any possible altercation I my encounter. My positive attitude toward life will also be helpful. I also have a good decision making skills, with out letting let and kind of prejudice entering my mind. The reason I am so positive about this is I've had such a variety of friends. I've had friends of different races, ethnic background, and religious beliefs. I've also have a vary strong emotional barrier due to all the tragedy I've gone though in my life. I've lost three of my closet friends in the last four years. Through all those years I was the person who was strong to help my entire friends threw our losses. When I get into the field all the knowledge I've acquired in my education will in all intent be usel ess. How do I know this you ask? Several people I know in several justice fields have told me that the real education comes in the field. Their have several movies such as "The Siege", Silence of the Lambs" witch have inspired me to follow in this possible life style. The T.V. show cops is the most instamental to me, due to the way they always explain how to dissolve every encounter, and what would happen to the perpetrator. These movies and T.V. shows give a positive outlook on my possible life in handling these issues. I would not leave out the possibility of becoming a scout leader. The reason I think I could handle this job is because I've always had an attraction to teaching children. I have been babysitting children for seven years, for my next-door neighbors and my aunt. Any other person besides their parents or me could always never control the three boys next door. Then there are my two cousins who were both girls. I always loved watching children; I just get along with the m so well. I believe I could make a difference in their lives.

WHITE BLOOD CELLS Essay -- essays research papers fc

White Blood Cells Bacteria exist everywhere in the environment and have continuous access to the body through the mouth, nose and pores of skin. Further more, many cells age and die daily and their remains must be removed, this is where the white blood cell plays its role.   Ã‚  Ã‚  Ã‚  Ã‚  According to this quotation, without white blood cells, also known as leukocytes, we would not be able to survive. White blood cells are our body’s number one defense against infections. They help keep us clean from foreign bacteria that enter our bodies. Statistics show that there are five to ten thousand white blood cells per micro liter of blood, however this number will increase during an illness. White blood cells can differ in many ways, such as, size, shape and staining traits. There are five different kinds of white blood cells that fall into two separate categories. One category is called, granular leukocytes, and the other is called agranular white cells.   Ã‚  Ã‚  Ã‚  Ã‚  There are three different types of granular leukocytes. Neutrophil is a phagocyte, produced in the bone marrow that ingests and destroys bacteria extremely fast. Neutrophil has a diameter, which is, about ten to twelve micrometers long. They make up about 60-70 percent of the total number of white blood cells in our body. Eosinphil is a type of white blood cell that secretes poisonous materials in order to kill parasites, allergies and phagocytosis of bacteria, which is when the...

Power Utility Consumption Capm in Uk Stock Markets

Pricing of Securities in Financial Markets 40141 – How well does the power utility consumption CAPM perform in UK Stock Returns? ******** 1 Hansen and Jagannathan (1991) LOP Volatility Bounds Volatility bounds were first derived by Shiller (1982) to help diagnose and test a particular set of asset pricing models. He found that to price a set of assets, the consumption model must have a high value for the risk aversion coefficient or have a high level of volatility.Hansen and Jagannathan (1991) expanded on Shiller’s paper to show the duality between mean-variance frontiers of asset portfolios and mean-variance frontier of stochastic discount factors. Law of one price volatility bounds are derived by calculating the minimum variance of a stochastic discount factor for a given value of E(m), subject to the law of one price restriction. The law of one price restriction states that E(mR) = 1, which means that the assets with identical payoffs must have the same price. For th is constraint to hold, the pricing equation must be true.Hansen and Jagannathan use an orthogonal decomposition to calculate the set of minimum variance discount factors that will price a set of assets. The equation m = x* + we* + n can be used to calculate discount factors that will price the assets subject to the LOP condition. Once x* and e* are calculated, the minimum variance discount factors that will price the assets can be found by changing the weights, w. Hansen and Jagannathan viewed the volatility bounds as a constraint imposed upon a set of discount factors that will price a set of assets.Therefore, when deriving the volatility bounds, we calculate the minimum variance stochastic discount factors that will price the set of assets. Discount factors that have a lower variance than these values will not price the assets correctly. Furthermore, Hansen and Jagannathan showed that to price a set of assets, we require discount factors with a high volatility and a mean close to 1. After deriving these bounds, we can use this constraint to test candidate asset pricing models.Models that produce a discount factor with a lower volatility than any discount factor on the LOP volatility can be rejected as they do not produce sufficient volatility. Hansen and Jagannathan find evidence that using LOP volatility bounds, we can reject a number of models such as the consumption model with a power function analysed in papers such as Dunn and Singleton (1986). 2 Methodology To test whether the power utility CCAPM prices the UK Treasury Bill (Rf) and value weighted market index returns, we first calculate the LOP volatility bounds.The volatility bound is derived by calculating the minimum variance discount factors that correctly price the two assets for given values of E (m). The standard deviations of the stochastic discount factors are then plotted on a graph to give the LOP volatility bound shown in figure one. Figure 1 here The CCAPM stochastic discount factors are then calculated for different levels of risk aversion. The mean and standard deviation of these discount factors are then plotted on the graph and compared to the LOP discount factor standard deviations.Pricing errors can then be calculated and analysed to see whether the assets are priced correctly by the candidate model. To accept the CCAPM model in pricing the assets, we expect the stochastic discount factors variance to be greater than the variance of the LOP volatility bounds. It is also expected that pricing errors and average pricing errors (RMSE) will be close to zero. These results will be analysed more closely in the later questions. 3 Power Utility CCAPM vs LOP Volatility Bounds In order for the power utility CCAPM to satisfy the Law of One Price volatility bound test at any level of risk aversion, the standard deviation f the CCAPM stochastic discount factor at that level of risk aversion must be above the Law of One Price standard deviation bound for the mean value of t he CCAPM stochastic discount factor at the same level of risk aversion. This is the null hypothesis and if it is accepted then the model satisfies the test. The alternative hypothesis is that it the standard deviation of the stochastic discount factor is below the Law of One Price standard deviation bound for the mean value of the stochastic discount factor.If the null hypothesis is rejected and the alternative hypothesis is accepted then the model does not satisfy the test. Table 1 here Figure 2 here Figure 2 shows LOP volatility bounds and the standard deviations and means of the CCAPM stochastic discount factors for levels of risk aversion between 1 and 20. It is obvious the standard deviations (Sigma(m)) of the CCAPM stochastic discounts factors are much lower than the LOP volatility bounds corresponding to the means (E(m)) of the CCAPM stochastic discount factors.This is true for any level of risk aversion, because the entire CCAPM (green) line lies below the LOP volatility bou nds (dark blue) line. Table 1 shows the standard deviations of the stochastic discount factors and the precise LOP volatility bound values, corresponding to the stochastic discount factor means so that the CCAPM can be formally tested. All of the standard deviations are lower than their respective volatility bound values. Therefore the null hypothesis is to be rejected and the alternative hypothesis is to be accepted for all levels of risk aversion between 1 and 20.Furthermore it would take a risk aversion of at least 54 to accept the null hypothesis. Therefore the power utility CCAPM stochastic discount factor does not satisfy the Law of One Price volatility bound test. These results are consistent with the equity premium puzzle study by Mehra and Prescott (1985). The study examines whether a consumption growth based model with a risk aversion value restricted to no more than 10 accurately prices equities. They have found that according to the model equity premiums should not excee d 0. 5% for values of risk aversion (? ) between 0 and 10 and values of the beta coefficient (? ) between 0 and 1. However the average observed equity premium based on the average real return on nearly riskless short-term securities and the S&P 500 for the period 1989-1978 was 6. 18%. This is clearly inconsistent with the predictions of the model. In particular if risk aversion is close to 0 and individuals are almost risk neutral, the model fails to explain why the sample’s average equity returns are so high.If risk aversion is significantly positive the model does not justify the low average risk-free rate of the sample. The results of Mehra and Prescott’s (2008) empirical study are consistent with our results, because the power utility CAPM did not satisfy our empirical tests. 4 Kan and Robotti (2007) Confidence Intervals The Law of One Price volatility bounds calculated in part 2 are subject to sampling variation. We have calculated point estimates of the volatilit y bounds, but we did not take into account that our results are based on a finite sample of Treasury Bill and market returns.To more accurately test whether the power utility CCAPM passes the LOP volatility bounds test, we need to identify the area in which the population volatility bound may lie. The area used is that between the upper and lower 95% confidence intervals for Hansen-Jagannathan volatility bounds obtained by Kan and Robotti (2007), shown in table 2. If the standard deviations of the CCAPM stochastic discount factors lie below that area for values of risk aversion between 1 and 20, then the power utility CCAPM model is to be rejected according to this test.Table 2 here Figure 3 here Figure 3 contains point estimates of the LOP volatility bounds, the standard deviations and means of the CCAPM stochastic discount factors for levels of risk aversion between 1 and 20 and the 95% confidence intervals for the volatility bounds. All of the standard deviations are below the ar ea in between the upper and lower confidence intervals for the volatility bounds. This indicates that at a 95% certainty the CCAPM does not satisfy the LOP volatility bound test even when sampling errors are taken into account. Performance of Power Utility CCAPM In recent academic literature on the subject of asset pricing models a common formal method of evaluating model performance is to calculate the pricing errors on a set of test assets. In this report the test assets are the Treasury Bill and Market Index quarterly returns from Q1 1963 to Q4 2009. The pricing error is calculated as [pic] Where [pic], [pic] Treasury Bill and Market Index returns, and [pic] is the pricing errors. Table 3 hereFor a model to correctly price an asset it would require that the pricing errors are as close to zero as possible since the pricing error is a measure of the distance between the model pricing kernel and the true pricing kernel. From Table 3 we can see that the pricing errors for the differe nt values of risk aversion are not close to zero and the size of the errors actually increases with the level of risk aversion. We can also see that the Route Mean Square Pricing Error (RSME) which measures the average distance from zero of the pricing errors is not as close to zero as we would hope and also increases with the level of risk aversion.If we note the case for a risk aversion level of 20 then the RSME is 6. 76%, since this is quarterly data this works out to an annual RSME of approximately 27%. With such large pricing errors we would not expect this model to perform strongly. Hansen and Jagannathan (1997) found that for different levels of risk aversion the pricing errors do not vary greatly. As noted above, this is not the case in our sample in which the error increases with the level of risk aversion, thus creating an ever wider dispersion of pricing errors.This is counterintuitive to what we would usually assume as with increased levels of risk aversion the consumer is only willing to accept a certain level of return for lower and lower levels of risk, therefore we would expect at some point that the mean variance level would pass the volatility bounds and therefore correctly price the assets. Conforming with this report Cochrane and Hansen (1992) found that in order to satisfy the levels of variance necessary to surpass the volatility bounds a risk aversion level of at least 40 was necessary.It should be noted that in reality this is quite unreasonable and also that for this level of variance to be attained the expected return might also have to drop below the level necessary to surpass the volatility bounds. Table 4 here From Hansen and Jagannathan (1991) we know that in order to price a set of assets correctly the stochastic discount factor (SDF) should be close to one and have high levels of volatility. Table 4 shows that SDF’s at low levels of risk aversion are relatively close to one but have very low levels of volatility.When the level of risk aversion increases the SDF’s get further and further away from one yet the volatility also increases. Therefore it seems reasonable to conclude that we would not expect any of these SDF’s to price the assets correctly. The results illustrated above are consistent with the earlier analysis and point to the conclusion that the power utility CCAPM does not do a good job in pricing the two test assets and thus does not perform well in UK stock returns. Cochrane and Hansen (1992) agree with this conclusion but Kan and Robotti (2007) find the opposite.The reason for this could be the use of sampling error in the Kan and Robotti paper and the different data used the in the analysis. This report illustrates that there exists not only an equity premium puzzle but also a risk free rate puzzle. This risk free rate puzzle as noted by Weil (1989) states that if consumers are extremely risk averse, a result of the equity premium puzzle, then why is the risk free rate s o low. Weil cites market imperfections and heterogeneity as the probable causes of this puzzle; however, this is not the explanation that Bansal and Yaron (2004) find.Using a model that accounts for investor reaction to news about growth rates and economic uncertainty they are able to go some way to resolving not only the risk free rate puzzle but also the equity risk premium puzzle. One method that could be used to improve the performance of the power utility CCAPM would be to construct the model using conditioning information; this would enlarge the possible payoff space available to investors. Kan and Robotti (2006) find that including conditioning information in models reduces the pricing errors by allowing the prices of volatility to move in line with the market.Although as Roussanov (2010) finds, conditioning information does not necessarily improve model performance and may actually exacerbate the problem. 6 Sampling Error in the Volatility Bounds When using the volatility bo unds as specified by Hansen and Jagannathan (1991) to test asset pricing models we must be wary of sampling error in the bounds. As noted previously if a model does not lie within the Hansen and Jagannathan volatility bounds then we can conclude that it does not price the test assets correctly.However, Gregory and Smith (1992) and Burnside (1994) first noted that this test does not take into account significant sampling variation and could therefore reject models that price assets correctly. Burnside (1994) uses Monte-Carlo simulation to illustrate that over repeated samples if sampling error is ignored the volatility bounds test performs poorly. Gregory and Smith (1992) state that the sampling error could be due to large variability in the estimated bounds or the use of sample data in the analysis.Kan and Robotti (2007) derive the finite sample distribution of the Hansen and Jagannathan bounds in order to take account of this sampling error. They argue that confidence intervals tha t take into account the variation can be constructed and used to test asset pricing models. The importance of this new method of testing cannot be underestimated as it could affect the decision to reject an asset pricing model or not, this is best illustrated with reference to examples. Kan and Robotti test the equity premium puzzle using data from Shiller (1989) to show the implications of taking into account sampling error.Through constructing the 95% confidence intervals for the Hansen and Jagannathan volatility bounds they are able to show that the time-separable power utility model being tested may not be rejected at low levels of risk aversion. This is in stark contrast to the findings when sampling error is not taken into account where the model is strongly rejected except for unfeasible levels of risk aversion. From Figure 3, as noted earlier, even when sampling error is taken into account for the model tested in this report it does not fall within the volatility bounds.Howe ver, it does decreases the distance between the model and the volatility bounds which is the major consequence of the Kan and Robotti paper. This new method goes some way to solving the problem noted by Cecchetti, Lam, and Mark (1994) who found using classical hypothesis tests that the Hansen and Jagannathan bounds without sampling error rejected true models too often. Again, an extension here could be to use conditioning information to improve the volatility bounds by using the methods of Ferson and Siegel (2003) and as a result hopefully reduce the sampling error in the bounds.References Bansal, R. and A. Yaron, 2004, Risks for the long run: A potential resolution of asset pricing puzzles, Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, 08. Burnside, C. , 1994, Hansen-Jagannathan Bounds as Classical Tests of Asset-Pricing Models,† Journal of Business & Economic Statistics, American Statistical Association, vol. 12(1), pages 57-79 Cecchetti, S. G. , P. Lam, and N. C. Mark, 1994, Testing Volatility Restrictions on Intertemporal Marginal Rates of Substitution Implied by Euler Equations and Asset Returns, Journal of Finance, 49, 123–152.Cochrane, J. H. and L. P. Hansen, 1992, Asset Pricing Explorations for Macroeconomics, NBER Chapters, in: NBER Macroeconomics Annual 1992, Volume 7, pages 115-182 National Bureau of Economic Research, Inc. Dunn, K. , and K. Singleton, 1986, Modelling the term structure of interest rates under Non-separable utility and durability of goods, Journal of Financial Economics, 17, 1986, 27-55. Ferson, W. E. , and A. F. Siegel, 2003, Stochastic Discount Factor Bounds with Conditioning Information, Review of Financial studies, 16, 567–595. Gregory, A. W. and G. W Smith, 1992.Sampling variability in Hansen-Jagannathan bounds, Economics Letters, Elsevier, vol. 38(3), pages 263-267. Hansen, L. P. and R. Jagannathan, 1991, Implications of Security Market Data for Models of Dynamic Economies, Journal of Political Economy, Vol. 99, No. 2 (Apr. , 1991), pp. 225-262   Hansen, L. P. and R. Jagannathan, 1997. Assessing specification errors in stochastic discount factor models. Journal of Finance 52, 591-607. Kan, R. , and C. Robotti, 2007, The Exact Distribution of the Hansen-Jagannathan Bound. Working Paper, University of Toronto and Federal Reserve Bank of Atlanta. Mehra, R. , and E. C.Prescott, (1985), The equity premium: A puzzle, Journal of Monetary Economics 15, 145-161. Roussanov, N. , 2010, Composition of Wealth, Conditioning Information, and the Cross-Section of Stock Returns, NBER Working Papers 16073, National Bureau of Economic Research, Inc. Shiller, R. , 1982, Consumption, Asset Markets and Macroeconomic fluctuations, Carnegie–Rochester Conference Series on Public Policy, Vol. 17. North-Holland Publishing Co. , 1982, pp. 203–238. Shiller, R. J. , 1989, Market Volatility, MIT Press, Massachusetts. Journal of Economic Behavior & Organization, Elsev ier, vol. 16(3), pages 361-364.Weil, P. , 1989, The equity premium puzzle and the risk free rate puzzle, Journal of Monetary Economics 24. 401-422. Appendix [pic] Figure 1 LOP Volatility Bounds. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. [pic] Figure 2 LOP Volatility Bounds with CCAPM.The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. It also shows the means and corresponding standard deviations of the CCAPM stochastic discount factors (green line) for values of risk aversion between 1 and 20. [pic] Figure 3 LOP Volatility Bounds with CCAPM and Confidence Intervals. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets.It also shows the means and corresponding standard deviations of the CCAPM stochastic discount factors (green line ) for values of risk aversion between 1 and 20. The figure contains the confidence intervals, with a 95% level of confidence, estimated by Kan and Robotti (2007) for E(m) between 0. 97 and 1. 0082 for the Law of One Price volatility bounds for their first set of test assets. The light blue line shows the upper bounds of the confidence intervals and the red line shows the lower bounds of the confidence intervals. Table 1 CCAPM stochastic discount factors’ means and standard deviations and corresponding LOP volatility bounds CCAPM |LOP volatility bounds |CCAPM | | |means | |st. dev. | | |0. 985121 |0. 82806186 |0. 011749 | |0. 980404 |1. 2067111 |0. 023503 | |0. 975849 |1. 57451579 |0. 035275 | |0. 971456 |1. 93015539 |0. 04708 | |0. 967223 |2. 27320637 |0. 58934 | |0. 963151 |2. 60350158 |0. 070853 | |0. 959239 |2. 92096535 |0. 082854 | |0. 955486 |3. 22555764 |0. 094953 | |0. 951893 |3. 5172513 |0. 107169 | |0. 94846 |3. 7960217 |0. 11952 | |0. 945187 |4. 06184126 |0. 132027 | |0. 942074 |4. 31467648 |0. 14471 | |0. 939121 |4. 5448604 |0. 15759 | |0. 93633 |4. 7812196 |0. 17069 | |0. 933701 |4. 99481688 |0. 184033 | |0. 931234 |5. 19520693 |0. 197645 | |0. 928931 |5. 38230757 |0. 211552 | |0. 926792 |5. 55602479 |0. 225781 | |0. 92482 |5. 71625225 |0. 240361 | |0. 923016 |5. 8628708 |0. 255322 |This table shows the means of the CCAPM stochastic discount factors for levels of risk aversion between 0 and 20, the corresponding LOP volatility bounds and the standard deviations of the CCAPM stochastic discount factors. Table 2 95% confidence intervals for E(m) between 0. 97 and 1. 0082 E(m) Lower Upper 0. 9700 3. 1823 5. 2069 0. 9710 2. 9385 4. 8383 0. 9719 2. 7038 4. 4830 0. 9729 2. 4781 4. 1411 0. 9738 2. 2617 3. 8125 0. 9748 2. 0544 3. 4974 0. 9757 1. 8565 3. 1959 0. 9767 1. 6680 2. 9080 0. 9776 1. 4890 2. 6337 0. 9786 1. 3195 2. 3731 0. 9795 1. 1597 2. 1262 0. 805 1. 0097 1. 8931 0. 9815 0. 8696 1. 6739 0. 9824 0. 7394 1. 4685 0. 9834 0. 6194 1. 2770 0. 9843 0. 5096 1. 0993 0. 9853 0. 4101 0. 9356 0. 9863 0. 3212 0. 7857 0. 9873 0. 2429 0. 6497 0. 9882 0. 1755 0. 5275 0. 9892 0. 1190 0. 4192 0. 9902 0. 0736 0. 3248 0. 9912 0. 0393 0. 2445 0. 9922 0. 0160 0. 1784 0. 9931 0. 0030 0. 1275 0. 9941 0 0. 0938 0. 9951 0 NaN 0. 9961 0 0. 0938 0. 9971 0. 0029 0. 1279 0. 9981 0. 0159 0. 1798 0. 9991 0. 0395 0. 2474 1. 0001 0. 0745 0. 3302 1. 0011 0. 1212 0. 280 1. 0021 0. 1796 0. 5408 1. 0031 0. 2498 0. 6689 1. 0041 0. 3317 0. 8123 1. 0051 0. 4255 0. 9714 1. 0061 0. 5309 1. 1461 1. 0072 0. 6481 1. 3368 1. 0082 0. 7769 1. 5437 This table shows the upper and lower bounds of the 95% confidence intervals Kan and Robotti (2007) calculated for the volatility bounds for their first set of test assets. The confidence intervals presented are for values of E(m) between 0. 97 and 1. 0082. Table 3 Pricing errors for the Treasury Bill (Rf) and the value weighted UK market index (Rm), and the Root Mean Square Pricing Error (RSME) for each level of risk av ersion Level of Risk Aversion |Error Rf |Error Rm |RSME | |1 |-0. 0104 |0. 0047 |0. 0080 | |2 |-0. 0152 |-0. 0001 |0. 0107 | |3 |-0. 0199 |-0. 0049 |0. 0144 | |4 |-0. 0244 |-0. 0094 |0. 0184 | |5 |-0. 287 |-0. 0138 |0. 0225 | |6 |-0. 0329 |-0. 0180 |0. 0265 | |7 |-0. 0369 |-0. 0221 |0. 0304 | |8 |-0. 0408 |-0. 0260 |0. 0342 | |9 |-0. 0445 |-0. 0297 |0. 0378 | |10 |-0. 0480 |-0. 0333 |0. 413 | |11 |-0. 0514 |-0. 0367 |0. 0446 | |12 |-0. 0546 |-0. 0399 |0. 0478 | |13 |-0. 0577 |-0. 0430 |0. 0508 | |14 |-0. 0606 |-0. 0459 |0. 0537 | |15 |-0. 0634 |-0. 0487 |0. 0564 | |16 |-0. 660 |-0. 0513 |0. 0590 | |17 |-0. 0684 |-0. 0537 |0. 0614 | |18 |-0. 0706 |-0. 0560 |0. 0636 | |19 |-0. 0727 |-0. 0580 |0. 0657 | |20 |-0. 0747 |-0. 0600 |0. 0676 | | | | | |The pricing errors above are calculated as [pic], where [pic], [pic] Treasury Bill and Market Index returns, and [pic] is the pricing errors. The RSME is simply the average pricing error of the stochastic discount factor for each level of risk aversion. Table 4 Summary Statistics for power utility CCAPM stochastic discount factor |Level of Risk Aversion |Average |St Dev |Min |Max | |1 |0. 9851 |0. 0117 |0. 9551 |1. 0436 | |2 |0. 804 |0. 0235 |0. 9214 |1. 1000 | |3 |0. 9758 |0. 0353 |0. 8889 |1. 1595 | |4 |0. 9715 |0. 0471 |0. 8575 |1. 2223 | |5 |0. 9672 |0. 0589 |0. 8273 |1. 2884 | |6 |0. 9632 |0. 0709 |0. 7981 |1. 3581 | |7 |0. 592 |0. 0829 |0. 7699 |1. 4316 | |8 |0. 9555 |0. 0950 |0. 7428 |1. 5090 | |9 |0. 9519 |0. 1072 |0. 7166 |1. 5906 | |10 |0. 9485 |0. 1195 |0. 6913 |1. 6767 | |11 |0. 9452 |0. 1320 |0. 6669 |1. 7674 | |12 |0. 421 |0. 1447 |0. 6434 |1. 8630 | |13 |0. 9391 |0. 1576 |0. 6207 |1. 9638 | |14 |0. 9363 |0. 1707 |0. 5988 |2. 0701 | |15 |0. 9337 |0. 1840 |0. 5777 |2. 1821 | |16 |0. 9312 |0. 1976 |0. 5573 |2. 3001 | |17 |0. 9289 |0. 116 |0. 5377 |2. 4245 | |18 |0. 9268 |0. 2258 |0. 5187 |2. 5557 | |19 |0. 9248 |0. 2404 |0. 5004 |2. 6940 | |20 |0. 9230 |0. 2553 |0. 4827 |2. 8397 | This table shows the average value, standard deviation, minimum and maximum for the stochastic discount factor at each level of risk aversion. ———————– 24th November 2011

College Student Stress Coping

College students facing academic, social, and other stresses such as finances and how to cope with them. The most common stress most college students face is the stress from the work load that is common in a higher education system. This is the major challenge, and you will be facing without the close guidance you may have previously enjoyed from a teacher or parent; Social stress seems to be more prominent with underclassmen leaving home and there support structure from home but can affect any college student.Most students find that the number one cause of stress for them is financially trying to juggle a full load, and work full time to support yourself and for other students pay for school. Academic stress seems to be what cause the biggest problem for most students. There are some things you can do if you would like to lower your stress level and make student life better. First thing you should do is get the right information from the very beginning. How many lectures, seminars o r laboratory sessions are you supposed to be attending?With your assignments make sure you know exactly what you are being asked to do? How many words you’re expected to write and what the deadline is for handing it in. Also try to set up your own space for academic’s to make it easier to focus and concentrate, if you have a noisy roommate try to find a place in the library or even a local coffee shop to frequent. These suggestions will help lower your academics stress which will make life easier for the college student.Social stress for college students is something else that is very common especially in new college students. Most social stress comes from trying to fit it or even trying to create a new social network because in most case’s this is the first time away from home and away from your entire support structure and comfort zone. With social stress one of the most important things to remember is to try and stay involved in different activities at school and get involved with different organizations on campus to meet new people.When getting involved with the organization’s on campus you’ll meet new friends and become more socially outgoing which will help greatly with social stress. Reducing the amount of social stress in college students, will lead to a more enjoyable and positive experience. Financial stress tends to be tohe root of most students problems whether it’s from overspending, lack of proper guidance, peer pressure, inexperience or many other factors. As a result just about everyone is bogged down with large debts increasing day by day.As students many have tried different things to cut down expense but still no luck. There are many ways to help lift the financial stress for college students like taking federal or private loans, grants and scholarships, and working during the summer vacation. By taking federal or private loans you get to choose the best one that is suitable for your need, if you dec ide to go for a private one then do enough research to make sure you’re not at a loss and end up paying more instead of benefitting from it.Grants and scholarships are another great ideas because these will help lift the burden of some financial stress. When applying for grants and scholarships keep in mind these are offered free by federal organizations for the benefit of students. Make sure to take advantage of the situation but you have to prove worthy for these aids. Getting summer jobs will also help lift some of that financial stress because you can do it in your off time and not stress with studying or school work but catch up and save some money!The best ways for college students to cope with the stress of being away from home, social stress, and financial stress. Academic stress seems to be one of the most common among students. Most college students find that the added freedom of being away from the structure at home and the more difficult classes to be extremely st ressful. Just remember try and create your own space for studying with a desk and a quiet place, if you have a noisy roommate find a nice place in the library or a nice local coffee shop to visit.Social stress also seems to be a major problem with college students. Trying to fit in and make new friends after leaving there comfort zone and support structure can be difficult. When starting at college try and get involved in activities to get you out and have a good time. Another great thing to do is get into some organizations to help meet new people and even become more outgoing to make the college experience more fun and less stress. Financial stress seems to be the biggest burden for college students trying to survive on your own and worrying about how you will pay for chool. Remember federal and private loans are a great way to lift stress off you but just do your research and find the one that will work best for your needs. Grants and scholarships are the best thing for most stud ents because they are offered for free from federal organizations but you must prove yourself for these aids Summer jobs are great for students who want to make a little extra cash on the side and lift the stress and worry of college off their mind. B y following some of these tips you are sure to reduce some stress in your everyday college life.