The Supernatural In Macbeth Essay Example For Students

The Supernatural In Macbeth Essay In Shakespeares Macbeth, specific scenes focus the readers attention to the suspense and involvement of the supernatural. The use of witches, apparitions and ghosts provide important elements in making the play interesting. Examining certain scenes of the play, it can be determined that as supernatural occurrences develop, Macbeth reflects a darker self-image. Macbeth experiences his first strange encounter of the supernatural when he meets the three witches in act one, scene one. After learning of his prophecies to become king, Macbeth states, Glamis, and Thane of Cawdor: The greatest is behind (still to come). (1. 3.117-118). Shakespeare uses foreshadowing, a literary technique, to suggest to his readers the character Macbeth will suffer a personality change. Macbeth also implies his first notions of plotting an evil scheme by this comment. We will write a custom essay on The Supernatural In Macbeth specifically for you for only $16.38 $13.9/page Order now After the prophecies of the witches revealed the fate of Macbeth, the quest of the throne will be his next victory. The witches reveal a fate for Macbeth and imply that a part of what will come to him must come, but they reveal no fate of evil-doing for him and never, even by suggestion, bind him to evil doing. , states literary critic Willard Furnham. Furnham declares the only power the witches obtain over Macbeth is the power of insinuation. By offering to Macbeth the idea of power, the witches push Macbeth to the next level of greed and evil that did not exist prior to the encounter. The murder of King Duncan initiates Macbeths second encounter with the supernatural when he witnesses a floating dagger. As Macbeth awaits the signal to make his way up the stairs, he sees the floating dagger and proclaims, Come, let me clutch thee. I have thee not, fatal vision, sensible (able to be felt) to feeling as to sight, or art thou but a dagger of the mind, a false creation, proceeding from the heat-oppressed brain? (2. 2.33-38). This apparition confuses and frightens Macbeth. He can not comprehend how he can see something and not be able to touch it. Thou leads me the way I was going; and such an instrument I was to use. And on thy blade and hilt, drops of blood which was not so before. Theres no such thing. It is bloody business which takes shape. (2.2.43-49) Here, Macbeth begins to question whether his mind is playing tricks on him. The situation seems quite coincidental considering he is minutes from murdering a man with a similar weapon. He states the apparition takes place due to the bloody business about to occur. The dagger symbolizes the point of no return for Macbeth. If he chooses the path in which the dagger leads, there will be no turning back. Macbeth fears Banquo due to his prophecy to father kings, so Macbeth proceeds to plot the murder of his once friend, which spurs yet another brush with the supernatural. Macbeth attends a banquet at which he witnesses the ghost of his dead friend. (3.4.37-145) The fortunes of the three witches sparked Macbeths desire to murder Banquo and caused him to dig himself into a deeper hole. Macbeths guilt and fear combined drive him to darker and more evil actions in an attempt to cover his past misdeeds. What man dare, I dare. Approach though like the rugged Russian bear, the armed rhinoceros, or th Hyrcan tiger; Take any shape but that (Banquo) and my firm nerves shall never tremble. (3. 4.100-104) Macbeth feels frightened at the sight of the bloody ghost haunting him and is angered that the ghost revealed it self to him. His guilt causes him to proclaim he could take on a rhino, tiger or any other wild animal, but not Banquos ghost. After his encounter with the ghost, Macbeth proceeds to visit the witches one last time to insure his security. .u0b73bd49196ac724f3718a05b1e1c5f9 , .u0b73bd49196ac724f3718a05b1e1c5f9 .postImageUrl , .u0b73bd49196ac724f3718a05b1e1c5f9 .centered-text-area { min-height: 80px; position: relative; } .u0b73bd49196ac724f3718a05b1e1c5f9 , .u0b73bd49196ac724f3718a05b1e1c5f9:hover , .u0b73bd49196ac724f3718a05b1e1c5f9:visited , .u0b73bd49196ac724f3718a05b1e1c5f9:active { border:0!important; } .u0b73bd49196ac724f3718a05b1e1c5f9 .clearfix:after { content: ""; display: table; clear: both; } .u0b73bd49196ac724f3718a05b1e1c5f9 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .u0b73bd49196ac724f3718a05b1e1c5f9:active , .u0b73bd49196ac724f3718a05b1e1c5f9:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .u0b73bd49196ac724f3718a05b1e1c5f9 .centered-text-area { width: 100%; position: relative ; } .u0b73bd49196ac724f3718a05b1e1c5f9 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .u0b73bd49196ac724f3718a05b1e1c5f9 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .u0b73bd49196ac724f3718a05b1e1c5f9 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .u0b73bd49196ac724f3718a05b1e1c5f9:hover .ctaButton { background-color: #34495E!important; } .u0b73bd49196ac724f3718a05b1e1c5f9 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .u0b73bd49196ac724f3718a05b1e1c5f9 .u0b73bd49196ac724f3718a05b1e1c5f9-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .u0b73bd49196ac724f3718a05b1e1c5f9:after { content: ""; display: block; clear: both; } READ: Most everyone thinks a veterinarian is just someon Essay After this last visit, Macbeth becomes overconfident and a tyrant, which cause his downfall. The use of supernatural in Macbeth, provides the suspenseful nature of his work. Without the witches, apparitions and the ghost, Macbeth could not have reached his downfall. The use of supernatural in Macbeth caused Macbeth to become a darker and more evil person with each paranormal encounter. BibliographyFarnham,Willard. The Witches. 20th Century Interpretations of Macbeth Ed. Terence Hawkes. New Jersey: Prentice Hall Inc., 1982 p.61-62

Read The Instructions In The Uploaded Picture Example

Read The Instructions In The Uploaded Picture Example Read The Instructions In The Uploaded Picture – Admission/Application Essay Example Creating a Reliable Database for Small Business Enterprises – Information Technology Information technology refers to the transfer and access of data through the use of computers. Small business enterprises need to create and design reliable databases for better services and loss minimization. For instance, if a small business man runs a chain of hotels or has a cafà © offering both take in and take away foods, it is prudent for them to design a database for his products (Reid 13). The logical database design will rely on different entities depending on what the cafà © provides most to its clients. The database should put into consideration customer preferences, price list; door delivery if available and a mode of ordering by the clients from the comfort of their homes. For effectiveness, the database should indicate details of the customers, those who buy takeaway, take in and those who prefer to order from their homes. Database will play a significant role for the business by creating a strong customer base and increase their loyalty in case the business person decides to give offers in times of special occasions for his clients. The database should contain all the sales of the day, enable a tracking functionality; this helps in reducing loss and theft from employees. The database should also contain all the details of every single employee working in the hotel/cafà © for easier reference (Reid 13). A reliable database for any single small business enterprise will change the dynamics of the venture, and increase customers hence increased profits. It’s a challenging aspect of the information technology, but every business person should be encouraged to implement it for the benefit of their businesses.Work Cited Reid, Gavin, Small Business Enterprise: An Economic Analysis, Routledge, 2002, Pg 13, Print.

Expository Essay

Expository Essay Expository Essay Expository Essay Expository essay writing should follow this simple structure: introduction, main text and conclusion. Writing expository essay you need to discuss topic and explain your point of view presenting illustrative material. In the introduction give background information on essay topic, to set the foundation for your main text. Expository essay should have a clear essay title and brief introduction.   Do not overload introduction with unnecessary information. You will have an opportunity to write all you want in the main body of your expository essay. Body of expository essay should be as detailed as possible.   However, you should not go beyond the narrowly defined topic as well as you should meet the specific word limit.   Writing an essay, start new paragraph with new idea; however, it should be linked to the previous one. Expository Essay Writing Writing an expository essay avoid using personal pronoun 'I' and passive voice. The final things you need to do before turning in an expository essay are proofreading and revision. Make sure that your expository essay does not fall out of the assigned topic.   If your expository essay is about nature, do not write about humanity.   You may mention the role of humans, but you should not focus is on humanity.   It can be used to support your main points, as an example; however, it should not be the focus of your expository essay. Custom Expository Essay We would like to offer you professional custom expository essay writing service.   Our writers are working hard to ensure high quality of every piece of writing.   Expository essay written by our writer will definitely meet academic requirements of your teacher.  We strive not to be late and assign a writer to help you within a very short period of time (less than 30 minutes).   Why to choose .com? We are honest and reliable, we are responsible and diligent.   Your questions are not left unanswered; your expository essays are not plagiarized.   We are young but educated and devoted writers. You may contact us 24/7 to get an answer on any question you may have.

How to Change the Oil in Your Car Essay Example | Topics and Well Written Essays - 1000 words

How to Change the Oil in Your Car - Essay Example Don't be scared, you don't need any high tech gadgets or tools to get the job done. Aside from the oil wrench, you probably already have all the other tools lying around in your garage. Just like with any other task, getting the right tools together is the first step towards successfully completing the job. The tools you will need (Memmer How to Change Your Oil...) include a combination wrench set (closed and open-ended), oil filter wrench, oil catch basin, a zip lock bag and a funnel. For the oil change itself you will need, a new oil filter and 4-5 quarts of oil (refer to your car manual for for grade and number of quarts). Any commercial oil such as Valvoline and Castrol will do the job. Since this work will require you to get down and dirty, you should wear pre-soiled work clothes in case any oil dropping come down on you. Surgical gloves on your hands for better grip on the oil plug and old oil filter are advised for safety reasons but not really necessary. As a further safety p recaution, have a jack and 4 jack stands handy. After getting all of the necessary tools and materials together, you are now ready to begin the change oil process. Now remember, hot oil will drain faster out of your car (eHow How to Change Your Motor Oil). ... Instead, you should jack up the car and place a stand underneath each of the 4 vital weight points (refer to car manual) in order to secure the car above you. Carefully position yourself under the car, making sure to protect yourself against any accidental oil drippings. Now you are ready to locate the oil drain plug. Carefully place the oil catch basin under the drain plug before unscrewing in order to insure that the old oil will not drain into the street or be absorbed into the soil. Now would be the best time to wear the surgical gloves if you want to because the first rush of oil out of that pan is going to be scalding hot. Remember to clean the drain plug then set it aside to replug the hole after the drain is complete. Check the plug for any sign of wear and tear. Replace the drain plug if necessary then replug the oil drain hole. Replugging the drain hole is best done by hand in order to insure that cross threading does not occur. Be sure not to over tighten the plug once you use the wrench to tighten it. Now take a breath, we are almost done. We've got only 3 more important steps to go. In order to fully drain the old oil from the car, you will have to remove and replace the old oil filter located somewhere underneath the car. It is usually positioned somewhere on the side of the engine. You will need the oil wrench to loosen and remove the old filter. Expect the remaining old oil to spurt out from this area so make sure that the oil catch basin is properly positioned to catch the oil. Since an incorrectly attached oil filter can lead to costlier repairs later on, make sure to inspect this area of the motor thoroughly. Using an old but clean

Project Management Issue Report Essay Example | Topics and Well Written Essays - 1250 words

Project Management Issue Report - Essay Example As the report declares SRP is related to the rightful promotion of human rights in companies. These rights include – child labor, union management, health and safety of employees, compensation concerns and employee exploitation. It is expected that companies follow a SRP programme maintain a strict and realistic approach to human rights standards. This paper stresses that SRP clearly enlists the need for elimination all types of employee discrimination at the work place. Any kind of unfair treatment needs to be strictly controlled and equal opportunity standards should be maintained. Quality employment techniques and practice cultures offered by the management reveals a strong dedication to socially responsible procurement programs. SRP promotes the necessary condition on companies and organizations to behave responsibly and in shared favor of the society. An effective corporate governance structure smooths the process of fair trading, transparency in company and shareholders laws, observance with appropriate laws and regulations. SRP promotes the initiatives by companies to enhance the variety of suppliers. This pertains to giving contracts to less privileged, underrepresented groups, women owned small and medium scale businesses and services, retired, disabled and minority communities. This in turn assist in creating jobs fo r this underprivileged section of the society and subsequently create a uniform levels of living standards to some extent in the society.

Cambridge Engineering Selector Essay Example for Free

Cambridge Engineering Selector Essay Apparatus Computer Cambridge Engineering Selector database program Theory The Problem Below is a brief description of the theory regarding this lab: Oars are light, stiff beams. They must also have reasonable fracture toughness (KIC) and acceptable price per unit mass (Cm). The performance index for a light, stiff beam is: M1 = E1/2/? Where E is the Youngs modulus and ? is the density. To select the best materials, perform two selection stages: (i) In stage 1, select materials with M1 7 (GPa) /(Mg/m) (ii) In stage 2, select materials with KIC 1 MPa. m and Cm 100 GBP/kg. CES Selector Materials for Oars: The solution The performance index for a light, stiff beam (M1) is plotted in stage 1. Density is plotted on the x-axis and Youngs Modulus on the y-axis. A selection line of gradient 2, through the point (1.0, 49) is plotted. The constraints on adequate fracture toughness and price are plotted in stage 2. Fracture Toughness is plotted on the x axis and Density on the y axis. A selection box whose upper left corner is at (1.0, 100) is defined. In stage 1, the line representing the performance index is moved up until only a small subset of records remains in the selection. Magnified views of the two selection charts are shown in figures M5.3.1 and M5.3.2 (results intersection and hide failed records on), and the materials passing both stages are shown in figure M5.3.3. Oars for competitive rowing are made from Spruce, or (better) Carbon Fibre Reinforced Polymer (CFRP). Low Tech oars have been made for centuries out of bamboo. Boron Carbide might be acceptable, but it would be too brittle, despite its moderate fracture toughness. Selection Stages The selection methodology behind CES Selector is described in section 1 of the online book CES In Depth Background on Selection Systems. The application of this selection methodology to a specific area (e.g. the selection of the optimum material for an engineering component) is dealt with in the section of CES In Depth for the relevant data module. Before any selection can be performed, the user must specify which of the data tables will be the Selection Table (e.g. Materials, Process etc). This is done in the Project Settings dialog box (or on the Welcome screen when CES is first opened). Only one table can be used for selection in a given project. The filter and form for each data table must also be specified. The recommended filter and form combination for each type of selection is listed below. Selecting records with Selector involves performing a series of independent selection stages. On each stage, the user selects a subset of records. Every record in the current filter for the Selection Table is considered during each stage, and the program automatically keeps track of all the results. One way to perform a selection is to use a Selection Chart. The two axes of a selection chart specify record attributes. The user selects the area of the chart that fulfils the selection criteria. One selection chart is used for each selection stage. A second way to perform a selection is to use a Limit stage, in which numerical limits for one or more attributes are entered in a table. Limit stages can be combined with graphical stages (using selection charts). A single functional requirement (e.g. the strength/density ratio of a material) can be represented by one stage in Selector. In many design situations it is necessary to identify records that satisfy several functional requirements simultaneously, for example high strength/density, high stiffness and low cost/kg for a material. In these cases Selector can perform several selection stages and the program will store the results of each stage automatically. The selection stages can be modified at any time if necessary. At the end of the selection (or at any other time), the user can find out which records passed all, or some of the selection stages. It is important to realise that in this strategy, all records contained in the selection table with applicable data entries are considered in every selection stage (and are plotted on the charts). Therefore each stage is independent of the others. This means that records are never discarded from the selection process, even though they may fail a particular selection stage. So it is possible to find out how every entity performed on each of the stages. The ones that pass all stages will probably be the best choices. Selector can also generate plots of user-defined attributes, which are mathematical combinations of the attributes in the database. Examples are the specific strength el / (el is the elastic limit and is the density), and the performance index for a light stiff beam E / (E is the Youngs Modulus). This facility greatly expands the versatility of the selection process and enables two complex performance requirements to be compared on one selection chart. CALCULATION OF THE GRADIENT FOR BOTH GRAPHS The gradient of the lines in both graphs were calculated using the performance index for the bending of rods, the formula used was: E/P = Youngs Modulus / Density In order to get the above equation into the correct term for a gradient or a curve (y=m x + c) both sides of the equation had to be logged: LOG E LOG ? = LOG C Transpose for LOG E LOG E = LOG ? + LOG C The equation for a straight line is y = mx + c From the above it is fair to mention that: Y = LOG E X = LOG ? M = 1 The performance used in this lab was E 1/2 / P = C If you take log on both sides of the equation above: 1/2 LOG E LOG ? = LOG C Transpose for 1/2 LOG E: LOG C + LOG ? = 1/2 LOG E Multiply both sides by 2 to get LOG E LOG E = 2 LOG ? + 2 LOG C From the above it can be assumed that: Y = LOG E M = 2 X = LOG ? C = 2 LOG C M (The gradient) = 2 The gradient in the first graph of Density Vs. Youngs Modulus is 2. If another performance index is used: K IC / p = c The log of both sides of the equation gives: 2/3 LOG K ic = LOG ? + LOG C Multiply both sides 3/2 gives: LOG K ic = 3/2 log ? + 3/2 log c Y = LOG K ic M = 3/2 X = LOG ? C = 3/2 LOG C M = 3/2 = 1.5 The gradient of the line in the second graph of Fracture Toughness Vs. Density is 1.5 Method The Cambridge engineering selector was the program that was used in order to get the desired materials. The main two properties that the chosen material requires are strength and toughness. In order to get the right material two graphs had to be plotted. The first graph was Youngs Modulus vs. Density and the second graph was Fracture toughness vs. Density. The first thing that had to be done was to select the right units, which are SI units. This was done by going onto Tools, selecting options and then the correct units and currency which was GB Pounds. Select New Project from the File menu Choose the New Graphical Stage command from the Project menu. The Graph Stage Wizard will appear, ready to define the x and y axis of your chart. The procedure for selecting attributes for plotting on the selection chart axes is the same for both axes (and whichever selection table). The steps are as follows: The X-Axis page is presented first. To specify a single attribute, click once on the down arrow to display the drop down list box. Select one attribute, Density. (The Advanced function could be used to create a combination of attributes for one axis.) The title can be changed by the user by typing in the Title field if desired. Let the settings for the axis Scale remain as the default, Logarithmic and Autoscale. You can change the scales to linear and back again, by clicking one of the radio buttons marked Logarithmic and Linear. To switch to the Y-Axis page click once on the other tab. Select the attribute Youngs Modulus from the drop down list box and let the scale be Logarithmic and Autoscale. Click once on OK to exit the dialog. The graph will then be created. Follow the same steps to create the other graph except this time, the y-axis is going to be Fracture toughness. Once both graphs are created, the gradient, which was worked out earlier, has to be put in. In the graph showing Youngs Modulus vs. Density the gradient is 2 and for the graph showing Fracture toughness vs. Density it is 1.5. To put in a gradient line, simply click on the icon on the toolbar which has a picture of a gradient on it. Then a box will appear asking you to enter a value for the gradient. Once the value of the gradient is entered, the gradient will appear on the graph. The line can be moved up and down the graph depending on what kinds of materials are needed. In order to narrow down the number of materials to a few, the line has to be moved carefully upwards until only a few materials are shown in the box. This has to be done for both graphs until only 4 or 5 materials appear in the box showing both stages. Results Engineering Materials Lab All Stages Name Identity Epoxy/HS Carbon Fibre, UD Composite, 0à ¯Ã‚ ¿Ã‚ ½ Lamina MXP_CFTSEPHCUD001 Low Density Wood (Longitudinal) (0.22-0.45) MNW_L_LD Medium Density Wood (Longitudinal) (0.45-0.85) MNW_L_MD PEEK/IM Carbon Fibre, UD Composite, 0à ¯Ã‚ ¿Ã‚ ½ Lamina MXP_CFTPPEICUD001 Conclusion After looking and analysing the results taken from the two graphs, the materials chosen were Epoxy SMC (carbon fibre), low density wood, and medium density wood. ). But in the economical end of manufacturing rowing boat oars, both the wood materials would be selected, as they are reasonably cheap to buy, whereas carbon fibre is more expensive. Costs of materials are not the only concern, as the usage of each material is just as important. Questions could be asked; such as, how often is the boat going to be used? Is it going to be used on a regular basis? All of these questions should be taken into consideration before a decision is made. If an average person who is not a professional rower was going to consume a rowing boat ore, he/she would be better off opting for the one made from low density wood as the wooden ore is a great deal cheaper. On the other hand if the same question was asked to a professional rower, then the rower would pick the ore made of carbon fibre since the price does not come at the top of the list of concern and winning the race is the major objective. Basically, there is a good point and a bad point on each material. This largely depends on the object of buying the ore. If it is to win a race then money is not an option and the consumer would be better off buying the one made from carbon fibre but if the object is to just go rowing for a weekend then the best option would be to go for the oar made from wood simply because there is no likely consistent further use for the ore.

Technical Documentation :: Computer Science

Technical Documentation The software that will be used for The Castletown High School System will be Microsoft Excel. This is a spreadsheet program that allows the user to carryout calculation and functions by using formulas. The machine should have a printer attached to it so that the users are able to print their work when they are done. The users of the system do not need to be overly good with computers but they do need a basic knowledge of how the system operates. The system has been designed to help those users who are not overly good with computers. This system should be opened by an icon on the desktop this will make it easier and quicker for the users to access. Each of the buttons included in the homepage has a macro attached to it which performs a specific task: Enter Results: This button takes you to a screen were you select which class you would like to enter results for when you chose the class you are then taken to the results entry form where you can enter the results for each pupil for your specific subject. Streaming List: This button also takes you to a page that asks you which class you would like to stream when you have chosen this you will be brought to the streaming list were you can stream that class but also compare the male and female results in a graph. Pupil Report: This button allows you to create a pupil report when you click this button you will go again to the class selection. When you chose the class you must then chose which of the pupils you wish to do a report on when you do this you will be brought to the report sheet were all the pupils details including grades will already be entered. Lookup(Grade,GradeLookupTable) This formula is used to work out the grade for the pupils. This is done by the formula checking the percentages against a table. ROUND(Mark/Max_Mark)*100 When the Mark and the Max Mark have been entered by the user the above formula will automatically work out the percentage. This button when pressed will bring the user back out of the Data Entry Form back to the main menu. Data Validation This is the error message that appears if the user inputs a number that is greater than the max mark. This is the basic layout of my report. Text Box: When the report is opened the information for Aine Boyle is automatically taken from the database and displayed in the streaming list, so the user only has to fill in there comment manually.

The Ku Klux Klan of the 1920’s

The Ku Klux Klan (KKK) was notorious for their hatred towards African Americans and their proclamation of white supremacy. They were known as the invisible empire and for their symbols of intimidation, which included white cloaks with hoods, and burning crosses. The KKK was depicted as an organization which was mostly active in the southern Confederate states and targeted African Americans. It originally died out in the late 1860s, but The Klan rose again in the 1920's because of the motion picture Birth of a Nation, new immigrants arriving to America, and hatred towards African-Americans .Birth of a Nation was a silent film that premiered in 1925 that was directed by D. W. Griffith. Griffith went to Johns Hopkins University where he met Woodrow Wilson and became good friends. Wilson was a supporter of the Klan. One of the slides in Birth of a Nation has a quote by Wilson that said,†The white men were roused by a mere instinct of self-preservation †¦ until at last there ha d sprung into existence a great Ku Klux Klan, a veritable empire of the South, to protect the Southern country. Dixon's was a legislator, baptist preacher, lecturer, novelist,playwright, and an actor. The movie is based on the 1905 book The Clansman: An Historical Romance of the Ku Klux Klan by Thomas Dixon (Chalmer 28). This story revolves around two polar opposite families; the northern Stonemans and the southern Camerons. In this story their sons and daughters fell in love but were split by the civil war stricken states and reconstruction had devastated them.Congressmen Stoneman (was based on radical republican Thaddues Stephens) was represented as a hate-filled villain, urged by his Mulatto mistress to degrade the captured south, and with the recent assassination of â€Å"The Great Soul,† Abraham Lincoln, there was nothing to stop his rage. According to the book the south was ruled by Black tyranny and black corruption ‘stained' the legislative hall. The opposite of Congressmen Stoneman was Ben Cameron, leader of the KKK and a civil war hero of the south.In the end the Klan comes and saves the innocent, avenges the fallen, and reunites the grand lovers (Binder 9:166). D. W. Griffith based the movie on Dixon's book, by re-staging the war battles, Sherman's march to the sea. This gave the impression that the Klan was the ‘savior' of the states and the patriots leading our country with an invisible fist. This inspired many people to be patriotic like the Klan but others wanted to be the Klan again. William J. Simmon was one who had viewed this movie and took it to heart. He thought that it was time to bring The Klan back. Colonel† Simmons plan for the Klan had been revealed in an advertisement in the Atlanta Journal on December 7 1915. It contained blurbs such as, â€Å" The world's greatest secret, social, patriotic, fraternal, and beneficiary order. †This helped make the Klan more popular, but it wasn’t the only reason for the KKK's substantial growth. There are many other things that led to the KKK success that fell into place beautifully. They were allowed to march in parades during World War I in demonstrations of patriotism. After the war the seized the opportunity for power. Binder 9:167) Many problems were caused by a new influx of immigrants across the United States. Race riots sprang up in Chicago, Omaha, Duluth, Springfield, Tulsa, Texas, Arkansas, Kansas, and Florida. The KKK disdained the new southern and eastern European immigrants that were. usually either Roman Catholic, Jews, Slavs, or Bolshevik. But they still hated people who were not white. This helped the KKK spread quickly through anti-Catholic socialist Wisconsin. The Catholics seemed to be real â€Å"threats† to the public schools and the enforcement of prohibition.The Klan actually favored something that may considered correct with there stance against alcohol during prohibition. The Klan went sour from there, when a few white men from Louisiana began criticizing them. These men where tortured and then later hanged by the Klan. This was known as The Mier Rouge Murders (Chalmer 29). The Ku Klux Klan spread to all corners of the United States, and all through the Midwest. William Allen White had experienced this first hand in 1921. He written of his experience and the experience of others.The following is from his letter that he had wrote on September 27, 1921. â€Å"An organizer of the Ku Klux Klan was in Emporia the other day, and the men whom he invited to join his band at $10 per join turned him down. Under the leadership of Dr. J. B. Brickell and following their own judgment after hearing his story, the Emporians told him that they had no time for him. The proposition seems to be: Anti-foreigners Anti-Catholics Anti-Negroes. There are, of course, bad foreigners and good ones, good Catholics and bad ones, and all kinds of Negroes.To make a case against a birthplace, a religion, or a race is wickedly un-american and cowardly. The whole trouble with the Ku Klux Klan is that it is based upon such deep foolishness that it is bound to be a menace to good government in any community,†(qtd Johnson 56). White went on to say how idiotic and self centered the Klan was by being so greedy and racial. He also said no one in Emporia fell into this recruiters clenches and they ran the recruiter out of town. (Johnson 285). The KKK had made there mark in many places.The KKK had control over many different government positions at the time such as in Indiana, Tennessee, Oklahoma, and Oregon to name a few, but in Indiana the Klan was very influential. In 1924, Republican Edward Jackson was elected governor. This made the rest of the state filled with members of the Klan, but this had not lasted long (AP 135-136). 1924 Anaheim, California was taken completely over by the Ku Klux Klan to make it a model of a ‘perfect' city, by taking over the city council, but it was short lived because the voters called for a special recall election.A little bit after this Earle Mayfield of Texas got the U. S Senators seat, this made the Klan very powerful in these regions(Chalmer 34). Klan members in government seats did not stop there. Franklin D. Roosevelt appointed a former Klan member as a Supreme Court Justice. This man was confirmed to be a big supporter of the Klan, this was Hugo Black. Hugo Black was from Alabama where the Ku Klux Klan had been growing rapidly. He joined the invisible empire and became a high ranking officer in the Klan. Later he entered into politics. He was supported by the Klan and prohibitionists alike.At the age of forty he had not been known all that well publicly in politics, but he had surpassed four other prominent candidates and won the Senate nomination in the democratic primary, which essentially assured him of victory. For the next year he campaigned in every County. As senator he had openly acknowledged Klan support and attended man y state wide rallies. When the Klan political power diminished he broke his ties with them in 1930 (Van Deer Ver). In 1937 Franklin Roosevelt was frustrated with the conservative members of the supreme court.His legislation to appoint one member for every justice over the age of seventy had failed after a bitter 168 day fight in congress. That plan would have allowed him to appoint as many as six new justices. Roosevelt was not finished yet, as the struggle created one vacant seat, which he had filled with Hugo Black (Leuchtenburg 1). The Klan during the time of Black's membership was very hateful to non white people, especially blacks. They had thought that their jobs were being snatched up by Black people. They also didn't like them because the Ku Klux Klan viewed anyone who was not white as inferior to them.It had been a hard life for a black person during this time period because of the political power and the number of members in the Ku Klux Klan, they also always used the Afri can Americans of scapegoats to their problems (Drowne 10). The downfall of the second wave Klan happened for a number of reasons but one main reason was the conviction of D. C Stephenson. Stephenson was a long time member of the Klan and became the high rank of Grand Dragon. He was Publicly known to be a strong Prohibitionist. In 1925 he went on trial for the murder of Madge Oberholtzer.He was also responsible for the abduction, forced intoxication, and rape of Ms. Oberholtzer. The court had ruled that He was sentenced to life in prison. This devastated the Klan and sent them on a steep decline of members. (AP 135-137) The Ku Klux Klan of the 1920s was very powerful during its prime. It started with Simmons, grew to enormous numbers, but then died out as quickly as it had came. The Ku Klux Klan had rapidly rose because of Griffith's major motion picture Birth of a Nation, the amount of new immigrants arriving to the United States, and the racial tensions between the Klan and African Americans.

Experiment on Animal Should Be Stopped

The issue on whether we should allow or not in Experimenting animals has been widely debated in our community recently. It is an important issue because it concerns misunderstanding and misleading data. Varity of different argument have been put forward about this issue but it is strongly agreed by most of the community that experiment on animals should be stopped.Scientist researches say that animal testing is the future to finding cures and helps them figure out what will work and not work on humans. Hence, it can help find cures faster and prevent more human death. Although some people believe that is true; I, therefore have different opinion. Reading through articles from different doctors made me realize that using animals in medical area hasn’t helped humans as what people think it has. In fact, their systems are not anything like ours.First and simplest statement is that animal experiments provide misleading data. At best, they tell us a good deal about how animals expe rience disease, but they rarely tell us something of value that can be applied to humans and it provides additional data, but not a higher level of accuracy. Another statement is that animal tests do not accurately predict how dangerous a drug will be in humans. In other words, drug tests on animals do not protect humans from harmful medications.It is hard to believe that after the horrible instances which have occurred, that they would continually use this procedure. Especially where it does no good, and harms defenseless animals as well. In addition to that, an animal virus can be 99. 9% similar to its analog in humans and still be completely different. To sum up, animal testing isn't helping us progress and if anything it is slowing us down. This, it is not necessary, nor helpful to continue to practice our medicines ; questions on helpless animals.

Mans Brutality Towards Man essays

Mans Brutality Towards Man essays Mans brutality to man comes from within. It shows how selfish and how inhibited a person can be. Mans inhumanit towards man can be cause by need of power , jealousy, and Greed. All of these reasons come from within. Examples of Mans inhumanity to man that is caused by need of power are World War One when Hitler wanted to take over the world and be the leader of the world and have the power to do anything he chose to. Another example is World War Two when Hitler wanted to take over the world again , but this time he had stronger powers on his side, but still he could not prevail. Another example caused by the need of power is the Holocaust when Hitler wanted to clear Germany of non pure Aryan Germans and Jews. Examples of mans unhumanity to man that is caused by Jealousy are the terrible insidents that happen at Columbine High School back in 1999. When two students who were jealous of their class mates chose to murder and kill in cold blood. Another example of mans unhumanity to man that is caused by Jealousy is Terrorism. That was shown to us on September 11th, 2001 when 2 Boeing 747 jets were hijacked and flown in to the twin World Trade Center Buildings. Both buildings collapsed and it is believed that approximately 5,000 lives were lost. Another example of mans unhumanity to man that is caused by Jealousy is the Ku Klux Klan. The Ku Klux Klan is jealous of other races because they are the working force of America. And the Population of Whites are falling in Major American cities such as Los Angeles , New York and Detroit. ...

Analysis of Optimal Conditional Heteroskedasticity Model

Analysis of Optimal Conditional Heteroskedasticity Model Abstract: Recently cryptocurrency markets have seen an immense growth. Bitcoin is one of the most popular cryptocurrencies accounting for the highest share of all cryptocurrency markets, even though it still remains rather unclear whether it resembles more to a currency, a commodity or an asset. Previous research has shown that Bitcoin is often used for investment purposes, a fact that suggests the importance of analysing its volatility. In this article, we examine the optimal conditional heteroskedasticity model, not only in terms of goodness-of-fit, but also in terms of forecasting performance, an area which has been underexplored in the case of Bitcoin. According to the results, the optimal conditional heteroskedasticity model that can fit the series is not the same as the one that can forecast it better. As modelling GARCH effects in Bitcoin market effectively is crucial for appropriate portfolio management, our results can help investors and other decision makers make more informed decisions. Keywords: Bitcoin, Cryptocurrencies, GARCH, Volatility, Forecasting JEL classification: C22, C5, G1 1. Introduction Over the last few years, the analysis of Bitcoin has drawn a lot of both public and academic attention. Bitcoin is the first implementation of a concept called â€Å"cryptocurrency†, which was first described in 1998 by Wei Dai on the cypherpunks mailing list, suggesting the idea of a new form of money that uses cryptography to control its creation and transactions, rather than a central authority, but the first Bitcoin specification was published in 2009 in a cryptography mailing list by Satoshi Nakamoto ( Bitcoin.org 2017 ). The market of cryptocurrencies has grown remarkably with Bitcoin being considered the most famous cryptocurrency, with an estimated market capitalisation of $ 19.6 billion (coinmarketcap.com accessed on 8th March 2017), which currently accounts for around 84.4% of the total estimated cryptocurrency cap italisation. An overview of Bitcoin can be found in, e.g., Becker et al. (2013), Dwyer (2015), Frisby (2014), Bà ¶hme et al. (2015) and Selgin (2015). Hence, Bitcoin is only briefly introduced here. It has been previously argued that Bitcoin shares some elements of currencies. However, recent fluctuations in Bitcoin prices (see Figure 1) have resulted in unpredictable volatility undermining the role Bitcoin plays as a unit of account (Cheah and Fry 2015), while users have adopted Bitcoin not only as a currency but also for investment purposes. In fact, new users tend to trade Bitcoin on a speculative investment intention basis and have low intention to rely on the underlying network as means for paying goods or services (Glaser et al. 2014). The Bitcoin market is thus highly speculative at present, and therefore Bitcoin may be mostly used as an asset rather than a currency (Baek and Elbeck 2015; Dyhrberg 2016a). Moreover, recent studies have examined the hedging capabilitie s of the Bitcoin (see, e.g., Dyhrberg (2016a, b), justifying the view of it as an asset, as well as the role of different exchanges in the price discovery process of Bitcoin (Brandvold et al. 2015), while it has also been previously shown that cryptocurrency markets share some stylised empirical facts with other markets, e.g., a vulnerability to speculative bubbles (Cheah and Fry 2015). Consequently, Bitcoin has a place in the financial markets and in portfolio management (Dyhrberg 2016a). Bitcoin has posed great challenges and opportunities for policy makers, economists, entrepreneurs, and consumers since its introduction (Dyhrberg 2016b), while Bitcoin price volatility seems to be a major concern for most of the general public at this time (Bouoiyour and Selmi 2016). As a result, studying Bitcoin price volatility is of high importance. Following the extensive literature on modelling financial asset prices using the family of Generalised Autoregressive Conditional Heteroskeda sticity (GARCH) models, recently there has also been an increased interest in modelling Bitcoin price volatility using similar methods. Previous studies have used different types of GARCH models when examining the Bitcoin price volatility.For example, the simple GARCH model has been employed by Glaser et al. (2014), Gronwald (2014) and Dyhrberg (2016a). On the other hand, other studies have considered extensions to the GARCH model in order to study asymmetries in Bitcoin price volatility. For instance, the Exponential GARCH (EGARCH) model has been used by Dyhrberg (2016a) and Bouoiyour and Selmi (2015, 2016), the Threshold GARCH (TGARCH) ( GJR-GARCH ) model has been employed by Dyhrberg (2016b), Bouoiyour and Selmi (2015, 2016) and Bouri et al. (2017), while the Asymmetric Power ARCH (APARCH) and Component with Multiple Threshold-GARCH (CMT-GARCH) models have been used by Bouoiyour and Selmi (2015, 2016). Nevertheless, it is rather unclear which conditional heteroskedasticity mo del should be used when studying the Bitcoin price volatility. Previous studies of the Bitcoin price volatility have focused mainly on the use of a single conditional heteroskedasticity model, without comparing different GARCH-type models , though , with the only exceptions being the studies of Bouoiyour and Selmi (2015, 2016), which have split [PK1] the sample into different sub-periods, though , and the study of Katsiampa (2017/forthcomng?), which has not considered the risk-return relationships, though [PK2] . In addition, little attention has been paid to forecasting the volatility of the Bitcoin prices. To the best of the author’s knowledge only the study of Bouoiyour and Selmi (2016) has examined the forecasting performance of the CMT-GARCH and APARCH models, but no study has compared the predictive ability of different GARCH models with regards to Bitcoin. Consequently, we aim to contribute to the literature by investigating which conditional heteroskedasticity mode l can describe and forecast the Bitcoin prices better. The remainder of the article is organised as follows: The next section presents the models employed in this study. The data and methodology used in the study are discussed in the third section, while the fourth section details our empirical results. Finally, the conclusions drawn and the implications are presented in section five. 2. Models In this section, the models used in this research are introduced. The models consist of an Autoregressive model for the conditional mean and a first-order GARCH-type or a GARCH-in-Mean-type model for the conditional variance [1] , as follows , , , where is the Bitcoin price return on day , is the error term, is a white noise process, is the conditional standard deviation, and hence is the conditional variance. When is equal to zero, the resulting model is the autoregressive model with a GARCH-type specification for the conditional variance, while when is different from zero a GARCH-in-Mean-type specification for the conditional variance is obtained. Adding the standard deviation to the mean equation measures the risk and helps with the identification and measurement of any risk-return relationship. The conventional GARCH(1,1) model is represented by , with , and . The GARCH model (Bollerslev 1986) is undoubtedly one of the most popular models for describing the conditional variance of financial returns. Nevertheless, since its introduction, there have been proposed many extensions of the GARCH model and there have been a lot of advances in modelling the conditional variance. Hence in this study, we also consider five extensions to the linear GARCH model, namely the EGARCH model of Nelson (1991), the TGARCH model introduced by Glosten et al. (1993), the APARCH model proposed by Ding et al. (1993), the Component GARCH (CGARCH) model of Engle and Lee (1999) and the Asymmetric CGARCH (ACGARCH) model. All these models constitute example s of extensions of the simple GARCH model and have attempted to describe the conditional variance more accurately. Moreover, compared with the simple GARCH model, the EGARCH, TGARCH and APARCH models allow for different volatility responses to opposite signs of the previous shocks. More specifically, the EGARCH model is defined as , and considers the asymmetric volatility responses to negative news, that is , and positive news, , as given by the sign of , if is different from zero. The TGARCH model is given by , where is the indicator function, with if and 0 otherwise, suggesting that positive shocks and negative shocks have again different effects on the volatility, if is different from 0. On the other hand, the APARCH model is defined as , where , , , and . This model imposes a Box-Cox power transformation of the conditional standard deviation process and the asymmetric absolute residuals (Ding et al. 1993). Furthermore, in contrast with the G ARCH model, the conditional variance of which shows mean reversion to , which is a constant for all time, the CGARCH model allows for both a long-run component of conditional variance, , which is time varying and slowly mean-reverting, and a short-run component, , and is defined as . Christoffersen et al. (2008) demonstrated that by including both a short-run and a long-run component allows the CGARCH model to outperform the GARCH model. Finally, the Asymmetric Component GARCH (ACGARCH) model combines the CGARCH model with the TGARCH model, introducing asymmetric effects in the transitory equation, and takes the following form , where is a dummy variable which indicates negative shocks, while positive values of suggest the presence of transitory leverage effects in the conditional variance. 3. Data and methodology The data consists of daily closing prices for the Bitcoin Coindesk Index from 19 th July 2010 to 10 th January 2017. The estimation sample cover s the period between 19 th July 2017 and 31 st December 2017 leading to a total number of 2357 observations, while the remaining ten observations are used in the forecasting sample. The Bitcoin CoinDesk Index is listed in USD and the data are publicly available online at http://www.coindesk.com/price. The data are converted to natural logarithms, and then the returns are defined as , where is the logarithmic Bitcoin price index change and is the daily Bitcoin price index on day . Figures 1 and 2 illustrate the Bitcoin prices and price returns, respectively, in the estimation period. We start the empirical analysis by producing descriptive statistics for the Bitcoin price returns, while the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit-root tests are also performed to examine the stationarity of the returns. As will be seen in the next section, the results show that the series is stationary. In order to choose the best model in terms of fitting to d ata, three information criteria, namely Akaike (AIC), Bayesian (BIC) and Hannan-Quinn (HQ), are employed. For given data sets, all of these information criteria consider both how good the fitting of the model is and how many parameters there are in the model, rewarding a better fitting and penalising an increased number of parameters. The preferred model is the one with the respective minimum criterion value. However, since model selection is often not only based on a model’s goodness-of-fit to data, but also on forecasting performance, it is important to also check the models’ predictive ability, as a better fitting model does not always lead to better forecasts. Hence, the best model specification in terms of forecasting is selected according to the Root Mean Squared Forecasting Error (RMSE), Mean Absolute Forecasting Error (MAE) and Mean Absolute Percentage Forecasting Error (MAPE), all of which are used as measures of forecasting performance. Although the RMSE is one of the most commonly used measures of predictive ability, the additional measures have been used in order to verify the results. [2] The models’ forecasting performance is evaluated based on out-of-sample forecasts, and model selection is examined in terms of both multi-step-ahead and multiple 1-step-ahead forecasting. The preferred model is the one with the lowest values of the measures of predictive ability. Fig. 1 . Daily closing prices of the Coindesk Bitcoin Index (US Dollars). Fig. 2 . Daily Bitcoin price returns. 4. Results Table 1 reports the descriptive statistics for the daily returns of the Bitcoin price index. The daily average closing return is positive and equal to 0.5805% with a standard deviation of 0.0606. Moreover, the returns are positively skewed, indicating that it is more likely to observe large positive returns, and leptokurtic as a result of significant excess kurtosis. The Jarque-Bera (JB) test confirms the departure from normality, while the results of the ARCH(5) test for conditional heteroskedasticity show evidence of ARCH effects in the returns of the Bitcoin price index. Therefore the Autoregressive model for the conditional mean needs to be combined with an Autoregressive Conditional Heteroskedasticity process to model the conditional variance. It can be noticed that the ARCH effects can also be observed from Figure 2 where large (small) price changes tend to be followed by large (small) price changes over time. Furthermore, the results from both the Augmented Dickey-Fuller and Phillips-Perron unit root tests indicate that stationarity is ensured. Table 1. Descriptive statistics and unit roots tests. Panel A: Descriptive statistics Observations 2357 Mean 0.005805 Median 0.000741 Maximum 0.528947 Minimum -0.388309 Std. Dev. 0.060607 Skewness 0.873024 Kurtosis 15.64823 JB 16010.55*** ARCH(5) 56.56059*** Panel B: Unit root test statistics ADF -46.90888*** PP -47.56848*** Note: *** indicates the rejection of the null hypotheses at the 1% level. Next, the estimation results of the GARCH-type models are discussed. The conditional mean equation includes a constant and an autoregressive term, while the conditional variance is modelled by various competing GARCH models. The model parameters are estimated by using the maximum likelihood approach under the Gaussian distribution. Table 2 presents the estimation results of each model. These include the model parameter estimates, the log-likelihood values and the three information criteria values. In addition, the ARCH(5) test to check whether the conditional heteroskedasticity is eliminated and the Ljung-Box test for autocorrelation with 10 lags applied to squared residuals, as well as the Jarque-Bera (JB) test of norm ality of the residuals have been used as diagnostic tests, the results of which are also reported in Table 2. According to the results, both the AIC and HQ information criteria select the AR(1)-ACGARCH(1,1) model as the preferred model in terms of fitting to data, followed by the AR(1)-CGARCH(1,1)-M and AR(1)-CGARCH(1,1) models, suggesting the important role of having both long-run and short-run components of conditional variance. The log-likelihood is also maximised under the AR(1)-ACGARCH(1,1) model. On the other hand, the preferred model according to the BIC is the AR(1)-CGARCH(1,1), followed by the AR(1)-ACGARCH(1,1) model. The latter result could be explained, though, by the fact that the BIC penalises more a higher number of model parameters, and hence the selection of the AR(1)-ACGARCH(1,1) model seems appropriate. It can also be noticed that for the AR(1)-ACGARCH(1,1) model all the parameter estimates are statistically significant. Moreover, the results of the ARCH(5) an d tests applied to the squared residuals of the AR(1)-ACGARCH(1,1) model indicate that the selected AR(1)-ACGARCH(1,1) model with Gaussian distribution is correctly specified because the hypotheses of no remaining ARCH effects and no autocorrelation cannot be rejected. Furthermore, despite the fact that the residuals still depart from normality, the value of the Jarque-Bera statistic associated with the residuals of the AR(1)-ACGARCH(1,1) model is much lower than the corresponding value for the raw returns. Consequently, the AR-ACGARCH model seems to be useful to describe the volatility of the returns of the Bitcoin price index. This result seems to be consistent with the study of Bouoiyour and Selmi (2016) [PK3] who found that the best model for the period from December 2010 to December 2014 is the CMT-GARCH model, which also includes both transitory and permanent components as well as thresholds related to positive and negative shocks. With regards to the out-of-sample for ecasting performance, the five- and ten-day-ahead forecasts as well as the five and ten 1-day-ahead forecasts of the twelve competing GARCH-type models were generated. We then compared the models’ forecasting performance based on the three mean loss functions (RMSE, MAE and MAPE). Table 3 reports the obtained results, while the bold numbers indicate the best model in terms of forecast accuracy. An interesting finding is that overall the information criteria for model selection in terms of goodness-of-fit do not agree with the measures of predictive ability. Even though the minimum RMSE values of the 10-step-ahead and ten 1-step-ahead forecasts were both given for the AR-CGARCH model, a result which is consistent with the Bayesian Information Criterion, the results of the other two measures of predictive ability (MAE and MAPE) showed that there are other models that perform better than the AR-ACGARCH and AR-CGARCH models when it comes to forecasting. More specifically, the minimum RMSE values of the 5-step-ahead and five 1-step-ahead forecasts were both given for the AR-EGARCH-M model. On the other hand, the lowest MAE and MAPE values of the 5- and 10-step-ahead forecasting as well as those of the five 1-step-ahead forecasting were all given for the AR-EGARCH model. The lowest MAE value of the ten 1-step-ahead forecasting was also given for the AR-EGARCH model, while the lowest MAPE value of the ten 1-step-ahead forecasting was given for the AR-APARCH-M model. In summary, according to our estimation results the AR-ACGARCH model is preferred to the other competing models in terms of volatility estimates for the returns. However, the preferred model in terms of forecasting is overall the AR-EGARCH. This result is crucial for portfolio management and decision making in general by individuals who use Bitcoin for speculative purposes. Finally, it should be noted that the model parameters were estimated under the Student- t and GED distributions as well, but as there was no improvement in either the goodness-of-fit or forecasting performance, the results are not reported here. [3] This is in contrast with the results of the study of Bouri et al. (2017) who found that the TGARCH(1,1) model under the GED density is the best fit. 5. Conclusions Over the last few years cryptocurrency markets have grown to a great extent, with Bitcoin having attracted a lot of attention from both the public and researchers. This article aimed to offer a discussion into Bitcoin price volatility by selecting an optimal GARCH-type model in terms of both goodness-of-fit to data and forecasting performance chosen among several extensions. It was found that even though the best model in terms of goodness-of-fit is the AR-ACGARCH, a result which is consistent with previous studies [PK4] , with regards to forecasting performance the best model seems to be overall the AR-EGARCH. Consequently, if the objective is to find the best model in terms of pr edictive ability, model selection based on information criteria only might not be adequate. As Bitcoin can combine some of the advantages of both commodities and currencies in the financial markets (Dyhrberg 2016a), it can be a useful tool for portfolio analysis and risk management. Hence, individuals in portfolio and risk management need to get a more detailed view of the Bitcoin price volatility. Our results may thus have important implications mainly for investors but also for other decision makers, such as policymakers, as they can enable them to make more informed decisions.

Sainsbury Assignment Example | Topics and Well Written Essays - 2000 words

Sainsbury - Assignment Example However, the major success factor of the company is its effective value chain framework that ensures long-term sustainability and profitability for the company (Sainsbury Plc, 2014). Notably, the company has fully automated depots in its inbound logistics which further depicts the extensive use of IT in its business operations. This not only enhances the operational efficacy of the company but also results in establishing it as one of the competent business unit in terms of implementing and utilizing IT (Sekhar, 2009; Clark, 2004). The operations of the company are conducted in a three store formats which includes the local, regular and the central domain. The company has more than 500 local stores operating in the UK that has diverse branding approach and fascia. The central formats are used for the stores of the company which are small to medium in size and operate in the center of the city. Apart from the regular products, the company also sells some specific mainline brands through its stores in the UK market (Prezi Inc, 2014; Khosrowpour, 2004). The outbound logistics of the company includes two picking centers and it follows a warehousing model. The aim of this model is to establish dedicated picking centers in the UK with the aim to enhance the delivery of its products and services to the urban area further expanding their services to new potential customers. Notably, the company has faced immense competition from the rival supermarket chains which has forced it to develop effective strategies which can ensure the sustainability for the business. This has influenced the company to adopt the warehousing model (Prezi Inc, 2014; Khosrowpour, 2004). In the value chain domain of marketing and sales, the company targets both the customers including high income group as well as the potential buyers falling under the low income group. However, the company mainly focuses on providing high end products and makes a mark for itself in the