Cry Freedom - 2804 Words

â€Å"Cry Freedom†: Chapter 1: Summary: Donald Woods is an editor of the Daily Dispatch, a journal in East London, South Africa. One morning he gets news of a police raid in the black township Crossroads which lies in Cape Town. He also gets photos of the raid and he decides to print them although the government doesn ´t allow to print such photos. Woods doesn ´t believe the demand of the black people but he is trained as a lawyer and doesn ´t like police brutality against black people. So he also writes an editorial about a Biko, the leadership of „The BCâ€Å". The next day Dr. Mamphela who works for Biko in his clinic, comes to Woods office to speak to him, about this article. She is very angry, because she thinks, that Woods has printed rubbish†¦show more content†¦Character | Quote | Conflict | Wendy * Donald’s wife * 37 years old * Play piano well * Slender * Vital body * More liberal politically than Woods | â€Å"From churches, abroad. From black people. Even some companies gave them some money.† | When she saw what the police was doing to black people, she convinced her husband to go to Kruger. As her husband, she is against injustice. When her husband told her about the book and that he had to leave the country, she cared at first about their children. She is a good mutter and she also cared for the security of Woods. | Chapter 4-5: Summary: In the black township, they eat with a black family. Biko, the family and are discussing about the system, the history how the white came to South Africa and how the black people got their bad situation which they have now. After this visit Woods change his mind, because he sees for the first time how worse the black has to live, how big their despair is but also how strong their togetherness stay. At the end of this day Biko and Woods become friends. Character | Quote | Conflict | Mapetla Mohapi * A black guy * Who took Woods to football match | As Woods looked at all those black faces, tired and unsmiling, he felt that the whole black world, which he had believed he knew so well, had a life he was totally unaware of. | He worked at the office where Woods work and he died during the story. HeShow MoreRelatedCry Freedom Essay1466 Words   |  6 PagesThis essay examines the film â€Å"Cry Freedom†, set in the late 1970s, which was directed by Sir Richard Attenborough in 1 987. The film was based on the true story written by Donald Wood, also one of the main characters in the film. The analysis will focus on the way the movie critically evaluates the political ideology that dominates the apartheid in South Africa. The essay will discuss the character’s and films attitude towards the white people and black people and how certain characters respond toRead MoreDr. Clark s Biko : Cry Freedom ( Bos Par1061 Words   |  5 PagesTen years after the death of Stephen Bantu Biko (1946-77), South Africa’s â€Å"Daily Dispatch† journalist, Donald Woods, wrote Biko: Cry Freedom (Bos par. 1). His book was subsequently adapted for film and produced by hollywood director: Richard Attenborough (Bos par.1). The film was released on the heels of South Africa’s nation-wide declaration of a â€Å"state of emergency† in 1986 (Clark and Worger xvi). Though some claim Attenborough’s film is a biographical look at the life, trial, and death of BikoRead MoreCry Freedom Synopsis1730 Words   |  7 PagesTong Gui Shuen S4 Science 1 CRY FREedom They said Steve Biko was a man of violence; then why did he talk of peace? They said he wanted revolution; so why did he talk of friendship? 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Huynh Pham HIST 1301 December 20, 2014 James McPherson, an American Civil War historian and also the author of the Pulitzer Prize- winning novel Battle Cry for Freedom, deliberates the reasoning behind what caused the Civil War in his book What They Fought For, 1861-1865. McPherson specifically examines what the Union and the Confederate soldiers fought for during the Civil War by reviewing the letters thatRead MoreBattle Cry Of Freedom By James M. Mcpherson1244 Words   |  5 PagesThe winner for the 1989 Pulitzer Prize for his book Battle Cry of Freedom, James M. McPherson, is a great american civil war historian. Born in Valley City, North Dakota on October 11, 1936 McPherson attended St. Peter High School. After graduation he attended Gustavus Adolphus College and in 1958 earned his bachelor’s degree with Magnum Cum Laude. He later attended John Hopkins University and earned his Ph.D. in 1963 and is now currently working as Professor Emeritus at Princeton UniversityRead More Martin Luther King And Patrick Henry: Cry For Freedom Essay545 Words   |  3 Pages Martin Luther King and Patrick Henry: Cry for Freedom Although Patrick Henry and Martin Luther King, Jr. are both skilled orators and use similar rhetorical devices to appeal to their audiences, they call for freedom for two totally different kinds of people. Both Patrick Henry and Martin Luther King, Jr. show their strengths as speakers through their use of these rhetorical devices. Among these are parallelism, allusions, metaphors, and rhetorical questions. Both speakers use theseRead MoreThe Movie Night And Boys Don t Cry 1637 Words   |  7 Pagespresentation of fictional or nonfictional story that gets the audiences a sense of the emotional trauma. 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The Data and Methodology used in the Hang Seng Index - Free Essay Example

Sample details Pages: 9 Words: 2665 Downloads: 10 Date added: 2017/06/26 Category Finance Essay Type Research paper Did you like this example? In this chapter, there is description of data used in this study, which are The Hang Seng Index (HSI), The Hongkong and Shanghai Banking Corporation Holding plc (HSBC Hldgs) and Oriental Press Group Limited. After that, methodologies of predictions based on past volatilities and GARCH class conditional volatility models are presented. Finally, I will talk about how to evaluate the forecasting performances in according to select the best models. Now, let me introduce the data analyzed in my study. 3.1 Data description 3.1.1 The Hang Seng Index (HSI) Global investors treat the Hang Seng Index (HSI) as an indicator of the performance of the stock market in Hong Kong. The HSI was publicly launched on 24 November 1969 and is one of the earliest stock market indexes in Hong Kong. It is now maintained by Hang Seng Indexes Company Limited, which is a subsidiary of Hang Seng Bank, one of the constituent companies. The HSI is a freefloat-adjusted market capitalization weighted stock market index. : The current price at Day t : The closing price at Day t-1 IS : The number of issued shares FAF : Freefloat-adjusted Factor, which is between 0 and 1 CF: Cap factor, which is between 0 and 1 It consists of 43 constituent companies which represent around 60% of the total market capitalization of the Hong Kong Stock Exchange (HKEX). In order to show the price movements of the major sectors of the market clearer, the HSI constituent stocks are grouped into four sub-indexes: Properties, Utilities, Commerce and Industry, and Fina nce. Normally, qualified potential constituents are companies with a primary listing on the Main Board of the Hong Kong Stock Exchange (HKEX). In recent years, more and more mainland China companies listed on the Hong Kong Stock Exchange (HKEX), they can also be one of the qualified potential constituents if they meet several conditions. The companys total ordinary share capital must be in the form of H shares, the shares of the company incorporated in mainland China that are traded on the Hong Kong Stock Exchange. And also, the company needs to complete the process of Share Reform first. As a result, the company has no unlisted share capital. As the market capitalization, the turnover ranking and the financial performances of the companies may change in different periods, the list of constituent stocks is reviewed quarterly. In this paper, the daily closing Hang Seng Index and the weekly Hang Seng Index are used. The data set ranging from 1 July 1997 to 30 June 2008 is retrie ved from Datastream, a U.K. incorporated data service company. The data set is partitioned into the in-sample estimation periods 1997 2007, the out-of-sample forecast periods 2007 2008. This separation provides 2467 and 522 in-sample observations for the daily series and weekly series respectively. For out-of-sample observations, there are 246 for daily series and 52 for weekly series. In order to obtain more accurate results, public holidays and special incidents leading the public announcement of non-trading, such as the black signal of the Rainstorm Warning System and no. 8 storm force wind signal of Hong Kongs Tropical Cyclone Warning Signals. During this period, there were quite a lot of economic events affecting the stock market in Hong Kong. Hong Kong is one of the victims of 1997 Asian Financial Crisis. After the HSI peaked at 16820, it was attacked by international speculators, leading to a 50% plunge. In 1998, the Hong Kong government intervention on stock market to p urchase component shares supported the market and the HSI rebounded to 18000. The rise in both the interest rate and the crude oil price burst the dot-com bubble in 2000. The HSI dropped to 14000 and then fluctuated between 14000 and 16000. 911 Incident occurred in 2001. The HSI kept falling and the minimum was 8894. The stock market started to recover after Mainland and Hong Kong Closer Economic Partnership Arrangement (CEPA) was signed on 29 June 2003. In August 2007, mainland China declared the plan to allow part of citizens to invest on Hong Kong market directly. In October, stimulated by the news of AH stock hedging program, the HSI break through 30000. Unfortunately, the HSI was affected by the fallen chain of major stock markets in the world and dropped 2061.23 on 22 January 2008. During this period, the average point of the HSI is 14313.39 and the stand deviation is 4563.68. The lowest point in these eleven years was 6660.42 which occurred on 13 August 1998. 31638.22 was the highest point and occurred on 30 October 2007. Don’t waste time! Our writers will create an original "The Data and Methodology used in the Hang Seng Index" essay for you Create order 3.1.2 HSBC hldgs Besides the HSI, one of the constituent stocks is randomly selected. At the beginning of the selection process, each constituent stock is assigned a number, 1 43. Then, a number, between 1 43, is randomly generated by Excel. If the assigned number of the company matches the generated number, that company will be selected and studied in this paper. The Hongkong and Shanghai Banking Corporation Holding plc (HSBC Hldgs) is the selected constituent company and belongs to the Finance Sub-indexes. HSBC hldgs is one of the most favourite stocks in Hong Kong. In 1865, it was founded in Hong Kong in March and in Shanghai one month later. The banks headquarter was in Hong Kong until 1992. Then it moved to London as a condition of the acquisition of Midland Bank in UK, and also due to the handover of Hong Kongs sovereignty. Currently, HSBC hldgs is both the worlds largest banking and financial services group and the 5th largest stock in the Hong Kong Stock Exchange (HKEX) by market capit alization. HSBC hldgs is also listed on the Bermuda, New York, London and Paris Stock Exchanges. It is a constituent of the FTSE 100 Index and the largest company listed on the FTSE. In 2010, CEO Michael Geoghegan moved to Hong Kong since HSBC hldgs focuses on Asia more now. Similarly, both daily and weekly closing prices of HSBC hldgs ranging from 1 July 1997 to 30 June 2008 are retrieved from Datastream. The data set is also divided into the in-sample estimation periods 1997 2007 and the out-of-sample forecast periods 2007 2008. The number of in-sample observations and out-of-sample observations for both daily and weekly series are also the same as the HSI series and the non-trading days are excluded. For in-sample observations, there are 2467 for daily series and 522 for weekly series. For out-of-sample observations, there are 246 for daily series and 52 for weekly series. During this period, the average closing price of HSBC hldgs is 96.92 and the stand deviation is 23.2 6. The lowest closing price in these eleven years was 40.48 which occurred on 21 September 1998. 140.586 was the highest closing price and occurred on 15 October 2007. 3.1.3 Oriental Press Group Limited In this paper, another stock which is not the constituent stock is also studied. The stock is selected randomly like the chosen constituent stock. Oriental Press Group Limited is selected and is incorporated in Hong Kong. The group takes part in the publication of daily newspapers, Oriental Daily News and The Sun, and The Sun Racing Journal. Oriental Daily News was first published in 1969. Today, the paper is at the top in the daily circulation and has a record readership of around 3,100,000. Unlike Oriental Daily News, The Sun targets at a younger and fresher reader groups. The Sun was published since 1999, and can also be found outside Hong Kong. By considering a lot of Hong Kong people living in North America and in order to develop the market of The Sun, there is a North America version in New York and Toronto. The Sun Racing Journal was established in 1991. It is one of the major horse racing magazines in Hong Kong. Like the HSI and HSBC Hldgs, both daily and weekly clos ing prices of Oriental Press Group Limited ranging from 1 July 1997 to 30 June 2008 are retrieved from Datastream. The data set is also divided into the in-sample and the out-of-sample with the same period as the above two series. The number of in-sample observations and out-of-sample observations for both daily and weekly series are also equal to as that of the HSI series and HSBC hldgs series. During this period, the average closing price of Oriental Press Group Limited is 1.499 and the stand deviation is 0.657. The lowest closing price in these eleven years was 0.533 which occurred on 23 June 1998. 3.175 was the highest closing price and occurred on 14 January 2004. 3.1.4 Transformation of Data As this paper is studying the volatility, I focus on the returns of the HSI and the stocks rather than the index and closing prices. Therefore, a transformation of data is needed before modeling. Data are transformed into daily returns by taking the first difference of the natural logarithm of the daily index and closing prices. : The daily return : The current price at Day t : The closing price at Day t-1 Mean equals to 0.00138 and stand deviation is 0.0176. Mean equals to 0.00163 and stand deviation is 0.0169. Mean equals to -0.00032 and stand deviation is 0.0315. 3.2 Methodology Stationarity If a time series can be estimated using time series models, it must be a stationary process, as non-stationary process cannot be studied directly. If the joint probability distribution of time series data keeps constant when time is moving, this data set can be said to be stationary and the mean and variance do not change over time. On the contrary, a non-stationary process whose joint probability keep changing in the period. The mean and variance vary at different time points. However, usually after a non-stationary series has been transformed, such as differencing or log-difference, the series become stationary. Accordingly, it is necessary to test whether the data set is under a stationary process before any further analyzing. The Augmented Dickey and Fuller (ADF) test is employed in this paper. The ADF test is an improved version of the Dickey and Fuller (DF) test. Both tests null hypotheses are the series with a unit root, which means the series is non-stationary. The DF te st is only based on a simple regression. where the residuals are followed the Dickey-Fuller Distribution. The hypothesis testing is The test statistic is After that, comparing the test statistic with the relevant critical value for the Dickey-Fuller test, if the value is larger than the critical value, the null hypothesis can be rejected, and the model is stationary; vice verse. The ADF test is based on a more complicated more. where is a constant, is the coefficient of a deterministic trend and is the summation of the lag orders of the autoregressive process. Historical mean Probably, the historical mean provides the easiest way to forecast the volatility. All the in-sample observations are used and equally weighted to forecast. where is the number of out-of-sample observations Moving average Under moving average method, the forecast of the volatility is given by an unweighted average of the in-sample observations. Unlike the historical mean method, not all the in-sample observations are used. The average is based on the in-sample observations over a particular time interval of fixed length. where T is called the moving average period or rolling window. Three different lengths are considered for each frequency. For daily data, three months, six months and one year are chosen. For monthly data, six months, one year and two years are chosen. Random walk Both historical mean and moving average methods assume that the volatility will be in stable or change slowing with a trend. But, if the volatility fluctuates unpredictably, the best forecast of the volatility of next period is the current real volatility. Exponential smoothing Under exponential smoothing method, the forecast of the volatility is a weighted average of the previous actual observation and the previous forecast. where is the smoothing factor, and must be between 0 and 1. If is equal to zero, the forecast exactly equals the prior actual observation and the exponential smoothing method becomes random walk. When approaches one, the preceding forecast is the majority. The value of smoothing factor is determined by minimizing the in-sample sum of squared errors. The estimated smoothing factors for daily data are between 0.64 and 0.95. For weekly data, the values lie in the range 0.88 to 0.92. Exponentially weighted moving average (EWMA) The exponential weighted moving average (EWMA) method is similar to the exponential smoothing method. The only difference is that the prior actual volatility is replaced by a moving average method. where is a smoothing factor lying between 0 and 1. Like the exponential smoothing method, the EWMA method becomes random walk if the smoothing factor is equal to zero. When the factor is getting closer and closer one, the moving average will be weighted heavier and heavier. For daily data, the estimated values of factors are in the range 0.001 to 0.53. The smoothing factors lie between 0.001 and 0.4 for weekly data. Generalized autoregressive conditional heteroscedasticity (GARCH) If the variance of the data is constant, the process is called homoscedastic. However, a lot of time series data in the real world have time-varying variance. In 1982, Engle proposed the autoregressive conditional heteroscedasiticity (ARCH) model to deal with time series data. Four years later, Bollerslev suggested the GARCH model. A GARCH (p , q) model is where , , and . There are joint estimations of both the conditional mean and the conditional variance process in the GARCH model. The error term, , follows a normal distribution with zero mean and the variance which varies with time. indicates the persistence of shocks to volatility. If the sum is equal to one, the GARCH model becomes the integrated-GARCH (IGARCH) model. Threshold-GARCH (TGARCH) In the GARCH model, it assumes that shocks with equal magnitude have the same impacts on the volatility no matter which sign shocks are, positive or negative. However, the different sign of the shock may have a different impact on the volatility. In order to parameterize this idea, the TGARCH model is one of a number of ways. The TGARCH model was proposed by Glosten, Jagannathan and Runkle in 1993. The model is express as where if , and if . Consequently, a positive shock has an impact of on the volatility, and a negative shock has an impact of on the volatility. If is greater than zero, the negative shock has an larger impact on the volatility and vice versa. quantifies the persistence of shocks. Asymmetric-GARCH (AGARCH) The AGARCH model is another way to parameterize the idea that different sign of shock with equal magnitude has a different impact on the volatility. The AGARCH model looks quite similar to the TGARCH model and is expressed as If is a positive number, the positive shock has a smaller impact on the volatility and vice versa. Exponential-GARCH (EGARCH) The EGARCH model (Nelson, 1991) does not have the non-negativity constraint on the parameter, and , in the above three GARCH models. And also, the EGARCH model is the third asymmetric kind of GARCH models in this paper. The model is expressed as where The positive value of indicates that the positive shock has an larger impact on the volatility and vice versa. 3.3 Forecast evaluation After the volatility has been forecasted, the forecasters need to be evaluated to see how small the forecast errors are. There are several loss functions for the forecast error. The choice of loss functions totally depends on investors preferences. Symmetric loss function The mean error (ME), mean absolute error (MAE) and root mean squared error (RMSE) are the easiest and commonest ways to measure the forecasting performance. The result of the ME can be an indicator showing the direction of overprediction or underprediction on average. As the errors by overprediction and underprediction offset each other, it is not surprising that the ME statistic usually is the lowest one among the others. Unlike the ME, the MAE does not have the problem of the offsetting effect. The RMSE provides a measurement if the investor prefers to impose a more heavily penalty upon the larger forecast errors. Asymmetric loss function As mentioned before, all investors have their individual preference for the loss functions. It is likely that investors have asymmetric loss functions rather than symmetric loss functions. Referring the past research (Pagan and Schwert, 1990; Brailsford and Faff, 1996), the mean mixed error is employed in this paper. Firstly, the mean mixed error with a more heavily penalty on the forecast errors by underpredicting is Secondly, the mean mixed error penalizing the overpredicted forecast errors move heavily is where O is the number of overpredictions and U is the number of underpredication in the out-of-sample forecasts.

ICT implementation report Free Essays

1. Range of data This is my data, which offers all the different cars and the details about them. The table is set out in and sorted by the specifications of the cars and also includes their prices. We will write a custom essay sample on ICT implementation report or any similar topic only for you Order Now 2. Creating order sheet 3. Customer details of order sheet This is the top part of my order sheet, which are the customer’s details and the company logo on the top right hand side of the page. 4. Setting up combo boxes I have created combo boxes so that customers can choose what they wish from my data of cars. Each combo-box category has 5 cars and an empty space left encase the customer chooses not to choose a car from a certain category. This selection is linked to the cell next to it as each car has a preset number and the number is what makes the cell named â€Å"cars† change to what is selected in the combo box. 5. Spinners The quantity is done using spinners. As the customer selects the type of car they prefer the price automatically comes up in the â€Å"price cell† and they can change the quantity up to 6 cars for each which is highly unlikely. As the quantity number changes the price is multiplied by the number in the â€Å"quantity† cell. 6. Totals After these processes are done the total price is calculated by multiplying the quantity chosen with the price of the car. 7. Lookups Column F uses a ‘VLOOKUP’ in order to find what car has been chosen by the customer. This is done by linking it with ‘Column C’ and this column shows a number which is the number of the car chosen on the combo-box and the number in the combo-box is the number which the cars are set as in the Data sheet. Hence displaying the selected car. There is also a VLOOKUPS done for the price which also works in the same way. 8. Tick box and IF statement for Online Discount This is the online discount tick box which is linked to the cell next to it as you can see above. As the box is ticked and un-ticked the cell next to it will change to TRUE or FALSE this enable me to create an IF statement formula that links to this cell in order to give discount or not. This IF statement is designed to give a 15% discount if B25 displays TRUE and if not then 0 will be shown. 9. Delivery YES, NO buttons and IF statement As shown in step 8 the Buttons are linked to the cell next to them. However this time the cell C28 will display either 1 or 2 which represent YES or NO. From this the IF statement in I24 can be done. If cell C28 is displaying 1 then a à ¯Ã‚ ¿Ã‚ ½6.99 charge will be put on the totals if number 2 is being displayed then cell I24 will come up as blank as there is no Delivery charge. 10. Tick box and IF statement Claims Bonus Discount This is the Claims Bonus discount tick box which is linked to the cell next to it as you can see above. As the box is ticked and un-ticked the cell next to it will change to TRUE or FALSE this enable me to create an IF statement formula that links to this cell in order to give discount or not. 11. Sub-Total This is a sum formula which adds all the prices which are displayed in the selected columns. 12. Working out VAT% Simply input the percentage I wanted as my VAT into cell H26. In the cell next to it a multiplication formula is used to multiply the percentage by the sub-total which leaves the VAT in cell I26. 13. Working out discount price. Simply input the percentage I wanted to discount into cell H25. In the cell next to it a multiplication formula is used to multiply the percentage by the sub-total which leaves the discount in cell I25. 14. Working out Claims bonus Discount price Simply input the percentage I wanted to discount into cell H27. In the cell next to it a multiplication formula is used to multiply the percentage by the sub-total which leaves the discount in cell I27. 15. Grand-Total This is a sum formula however is more complex because it is subtracting discounts and adding different prices. This formula automatically alters and changes according to what is displayed in the cells above it. 16. Add Macros These macros are recorded to copy the details of the chosen car from each row and insert them on to the invoice sheet. They are also set to change the font option and insert a new row so that cars which are chosen do not replace each other so that the user can select more then one car or extra. 17. Go to Invoice Go to Order These macros have been set up so that the user can toggle between the two sheets without any hassle. They are both very simple macros designed so that the user can change or add anything they want to their receipt. 18. Print Macro This has been created in order to present the user with a print menu once it is selected or print as soon as it is pressed, so that they can select their print option or directly print the receipt. 19. Logos, company details and picture This print scheme shows the company logo and the colour scheme which is matching throughout the project and the picture in order to make the project more attractive. Part of the logo is the company details that are merged with it. 20. Invoice number and date. Located below the logo, this is one of the essentials for the receipt, however there is no formula or calendar option used so the date has to be manually changed every day and the invoice number every time the invoice is being saved and printed. 21. Order details These details are automatically pasted from the order sheet using the Add Macros. Therefore the titles are set in that order according to the data sheet and the font has been set to be changed as part of the processes of the add macro. 22. Customer details on the invoice. These formulas simply display the information/details which the end-user would fill out at the top of the order sheet. 23. Calculation formulas on invoice The print screen above shows all the formulas used I order to calculate the grand total. These are the same formulas used as the order sheet which I have explained in detail, however these have different cell references and copy the if statements from the order sheet so that the customers order is transferred correctly and so I do not have to repeat the process again. How to cite ICT implementation report, Papers