The Monsters in My Head, Frank Langellas Essay

The Monsters in My Head, Frank Langellas Essay The Monsters of Life In Frank Langellas essay â€Å"The Monsters in My Head,† Langella describes fear as a monster our imagination that changes as we get older, Langella also describes how one should confront and control the â€Å"monsters† that are in our heads. I agree with Langella, that one should not fear the â€Å"Monsters† of life that one should confront or overcome fear itself because, if one does not overcome these monsters, these monsters will end up eating us or hunting us for the rest of our lives. In Langellas essay â€Å"The Monsters in My Head,† Langella describes that when he was a young kid, a mummy would come into his room every night to hunt him down, but then one night the mummy did not show up for its nightly routine, it had disappeared. Years and years passed till, one night when Langella already had a son, a four year old. Another monster showed up to eat up his sons sleep. Langella went into action with his macho strategy of fighting his sons monster with a pillow. So, from that night on he would always do his heroic achievement, fighting his sons monster off. After weeks of this continuing battle, Langella realized that the monster would return every time his son wanted it to return. Langella then reflected on his mummys disappearance and realized that his own monster had never gone away, it was always there next him, but it had changed shapes and sizes as rapidly as he grew older. As he grew older, Langellas monster went from a mummy to a flying object. The n it changed to a first date, a first rejection and then to marriage and now fatherhood. Then Langella told his son that he was not going to fight the monster anymore because it was his sons monster and he had to fight it himself since the monster was in his sons head, and only his son could control it. The monster never returned to hunt and eat up his sons slumber. It actually changed its form. The monster became his sons new favorite playmate. So, Langella attempts to suggest strategies to overcome and control the â€Å"monsters† or the fears of life. Langellas arguments of controlling and overcoming ones fears are true because I have experienced these â€Å"monsters† or these fears myself through my lifetime. When I was about ten, I used to dread watching horror films because after watching these gruesome-massacring films, I would always relive those scary-horrid scenes in my own dreams or as we well known them as nightmares. So, every time when my family wanted to watch these crimson-thrillers, I would just go to my room and watch cartoons to invade these things we call nightmares or monsters, that will come in the pitch-black night to eat us or hunt us down. One night, my uncle Rodolfo came over to watch the Boogeyman. My uncle told me not to be afraid of fictional-monsters that only existed in my head. So, I stayed that night to watch this terrifying -cliffhanger movie. As I anxiously watched the movie, I realized that the main character, Tim, was also afraid of this monster, the boogeyman, which Tim believed that it li ved in his closet, and would come out at night to terrify Tims sleep away. The point is that one day Tim decided that he wanted to confront this monster, so in other words he wanted to be brave and take control. As I watched the movie, I reflected and realized that I could also control and overcome my fear for screaming-suspense moving pictures or in other words horror movies. Then I told myself everything is in my head these monsters do not exist, they are imaginary. So, from that night on, I always enjoy the thrilling sensation of getting my hairs spike up after seeing a great scary movie without having any monsters invading my wondrous dreams. Like Langella said, we should overcome, control, and fight our own monsters, just like how I had to learn to fight and control my fear of having nightmares hunting me down after watching a horror movie. One has to always remember that these â€Å"monsters† or â€Å"fears† are just in our heads. Langellas argues that these â€Å"monsters† still stand next to us side by side every day, every hour, every minute that the clocks runs, these monsters never go away. They just change shapes and sizes. My monsters are always with me. They are my favorite companions with whom I go to school, my classes, and my every day activities. My monsters are my challenges and fears, my ups and downs; they are what keep me going. The â€Å"monsters† that only exist in our heads are what makes us better persons. Some of the many monsters that have accompanied me through my long journey of life range from high school to adulthood and now to UCR. High school was like that long double twisted rollercoaster that never ends but irony it went by in a flash. High school was like the â€Å"IT† of the wondrous carnival of life. The â€Å"monsters† of high school and adulthood were very judging and responsible for hunting me down but, I was able to conquer them by changing their appearances. High school became my stepping stone to high education and adulthood became my sense of responsible and maturity. Now UCR will become my dearest best friend and one of my new companions in this long journey. Like Langella said, we should not let the monsters in our heads control us we should take control of our fears of losing or failing. Therefore, Langellas suggestions on how the â€Å"monsters† in our heads are just fictional characters of our imaginations going wild. That change as we grow older in maturity and responsible. Lastly we should not let the â€Å"monsters† of life control us, we should actually take gear, control, and override them or else they will run us over.

Exegesis on Request of James and John in The Bible :: essays research papers

Exegesis on the Request of James and John 10:35-45 The context call of Christian discipleship is from chapters 8:22 – 10:52 these chapters are devoted to informing us of the disciples following Jesus and includes to miracles. These miracles emphasize that the disciples at this stage have no real knowledge or belief of the mystery of the son of man or his destiny and theirs. This passage â€Å"the request of James and John† is placed after â€Å"a third time Jesus foretells his death and resurrection†, and before â€Å"the healing of the blind Bartimaeus.† â€Å"A third time Jesus foretells his death and resurrection.† This is third passion prediction of Jesus addressing to his disciples about his fate and destiny to die, it also happens to be the most graphic and vivid described than the other two. It opens with Jesus and his disciples on the road to Jerusalem, and his disciples following him were afraid. Jesus took them aside and begins describing his death and resurrection. The cause of the disciples fears it that they do not know Jesus’ destination or his predictions. The disciples are blind in their understanding of Jesus’ Messiahship, their interpretation of a messiah is typical in all the other Jews minds. That God promised a messiah in the line of David, and would lead the Jew’s to victory in every battle, and fight it for them. In this sense the disciples expect that Jesus is going to throw out the foreign aliens (Romans). However it is through suffering, dying and the resurrection that the real truth or message of Jesus is revealed. â€Å"The healing of Blind Bartimaeus† 10: 46-52 This is the last healing miracle on the gospel of Mark. The Blind beggar addresses the Jesus as the son of David recognizing him as the messiah, and as â€Å"Jesus† as well. Unlike all the other miracles in the gospel Jesus does not try to silence the man but has already accepted the fact that he will die. The disciples throughout the gospel have shown antagonistic traits such as, fear doubt and a displacement of trust in Jesus. They have come up with ideas that will assure their own advancement. Bartimaeus recognizes Jesus as the son of Man, God’s messiah and followed Jesus on the road. This passage is placed here to highlight the disciple’s failure of not knowing the true meaning of the kingdom of God. Jesus teachings are not laws but an invitation into the kingdom of God through suffering and serving.

The Life of the Governess Rebecca Sharp :: Victorian Era

The Life of the Governess Vanity Fair Sets the Stage â€Å"If Miss Rebecca Sharp had determined in her heart upon making the conquest of this big beau, I don't think, ladies, we have any right to blame her†¦Ã¢â‚¬  (Thackery 27). The narrator of Vanity Fair encourages readers not to blame Rebecca Sharp for being determined to win Joseph Sedley's attentions and proposal in only ten days! After all, the narrator reminds us that she was motherless, and thus had no one to help her secure a husband. Yet, members of Vanity Fair rebuke Miss Sharp for her assertive efforts. Perhaps, though, one should sympathize and applaud Miss Sharp's labors because her destination after ten days was the life of a governess. A Governess-A Definition The position of a governess required that one act as a companion for her charges and teach them the accomplishments that would enable them to compete effectively in society†¦ The required accomplishments were still one or two languages, preferably French and Italian, music, dancing, drawing and needlework†¦ The eventual aim was the best possible marriage. --Alice Renton, 48 The governess was even often the heroine for writers focusing on domestic, educational and social issues (â€Å"The Victorian Governess†). Yet, author and former governess Charlotte Brontà « wrote, â€Å"it was better to be a housemaid or kitchen girl, rather than a baited, trampled, desolate, distracted governess† (Damrosch 1524). And Anna Jameson wrote, â€Å"a woman who knows anything in the world would, if the choice be left to her, be anything in the world rather than be a governess† (Renton 59). Why the Negativity Regarding a Governess? As the cries of these governesses allude, life as a governess was not always glamorous, despite the literary regard. â€Å"A governess who was capable of teaching more than the usual subjects was generally little valued† (Renton 50). The pay a governess received often reflected the small value. â€Å"Her wages could be as low as eight pounds a year†¦ Charlotte Brontà « received twenty pounds per year (actually only sixteen since washing expenses were deducted at the source)† (Allingham). Perhaps the Quarterly Review best put the institution of being a governess in perspective when the following was published, â€Å"a being who is our equal in birth, manners, and education, but our inferior in worldly wealth† (Renton 96). Thus, governesses â€Å"ranked with the superior servants† (Altick 56) and ended up feeling broken and lonely as Jameson described (Renton 59). So Where Did Becky Fit In? Becky was obviously not the typical Victorian governess.

Power Utility Consumption Capm in Uk Stock Markets

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

The Systematic Phonics Case Education Essay

It is apparent from the epoch of 80s and 90s where rational bookmans and instructors presented new influential thoughts sing instruction of English. The field of instruction of reading is a topic that is immensely researched and still under farther research. Harmonizing to Pearson ( 2002 ) , â€Å" Man-made phonics was the chief method of learning since the beginning of twentieth century, this type of learning comprises exercising of larning missive names, sounds of letters and after so intermixing of these † ( pg, 1 ) . As the twentieth century progressed, it brought more extremist alterations of attack. Smith ( 1971 ) focused on the country sing the development of the capableness to read. Harmonizing to him, the reading has something that an person learned to make instead than something an person was taught. Man-made phonics is non a new of learning reading, in fact, its function of being outstanding and popular instruction has been an unintended result of the whole linguist ic communication acceptance attacks in British schools. Harmonizing to Rutter ( 2006 ) , every bit far as man-made phonics is concerned the rating of research has to be set long term and it needs to be made certain that kids with larning troubles should be assisted with different ways in order to do difference. Stuart ( 2006 ) emphasized that new options has to be seek so that recommendations on national degree could be made, Stuart made this suggestion to Rise â€Å" the current research grounds is non sufficient for leting reliable judgements of the effectivity of implementing different attacks to systematic structured phonics learning † ( Stuart, 2006 ; p11 ) .Systematic phonics CaseAmerican National Reading Panel ( NRP ) , in footings of research grounds and instruction of reading related inquiries, was amongst the critical subscribers who reported in learning kids in English reading ( NICHD, 2000 ) . The study consisted of inquiries sing the early literacy such as ‘Does systematic phonics direction aid kids learn to read more efficaciously than non systematic phonics direction or direction learning no phonics? ‘ ( P92 ) . ‘Are some specific phonics programmes more effectual than others? ( P93 ) . The decision from these inquiries was that ‘specific systematic phonics programmes are all significantly more effectual than non-phonics programmes ; nevertheless, they do non look to differ significantly from each other in their effectivity although more grounds is needed to verify the dependability of consequence sizes for each programme ‘ ( NICHD, 2000, pg93 ) . In another case, a comprehensive instruction of reading attacks research was commissioned by England ‘s Department for Education and Skills ( DfES ) in order to polish the NRP methodological analysis by bring forthing a randomised controlled tests ( RCT ) tests. Research workers in their work concluded that grounds has been seen in RCT surveies which could turn out the effectivity of one signifier of systematic phonics compared with that of other ( Torgerson et al, 2006 ) . Rutter ( 2006 ) besides commented that the of import facet of determination is that RCTs are one signifier of optimum research conditions. The existent message that is apparent from the meta-analysis, carried out by NRP and its limitation to RCTs, is the thought of importance of literacy acquisition every bit far as systematic instruction of phonics is concerned ( Torgerson et al, 2006 ) . Rose ( 2006 ) besides agreed with this decision by saying that â€Å" the importance of systematic phonic work is huge and could be more effectual if incorporated with man-made attack, after looking at its grounds which is wide-ranging † ( Rose, 2006, p20 ) . However he emphasizes that regardless of commonalty amidst systematic phonics and man-made phonics, it is therefore the man-made phonics which can offer much better class to going skilled readers for early scholars ( p19 ) .Man-made phonics instanceTwo surveies were reported by Johnston and Watson ( 2004 ) , in which the 2nd experiment was carried out before the first one and is of greater importance since it is related to intercession. This intercession varied from normal to extra schoolroom tuition get downing six hebdomads after school entry. The excess preparation kept on traveling for 10 hebdomads holding 2 categories per hebdomad wholly consisted of 114 printed words. The one group was taught 2 letters per hebdomad by agencies of assorted games played where kids matched images and words by merely pulling their attending to initial w ord sounds and letters of those sounds while other group was taught in all places of the words such as enhanced acquisition and blending of the missive sounds in all places, while being taught 2 letters per hebdomad every bit good ( Johnston and Watson, 2004, p347 ) . What writers concluded was that the group with man-made phonics were far much better in footings of reading and spelling every bit compared to analytic phonics group and therefore, the man-made phonics proved to be effectual attack to learning spelling, reading phonemic consciousness as comparison to analytics phonics ( Johnston and Watson, 2004 ) . Rose ( 2006 ) besides stresses that man-made phonics gives more indispensable accomplishments that allows the bulk of students to read and compose in front of their chronological age. Harmonizing to her, the 20 per centum pupils who have jobs with literacy still have better foundation of the reading rudimentss and merely necessitate excess clip and engagement.Pearson ‘s FindingssPearson ( 2003 ) became portion of fact-finding programme to derive some penetrations of the kids sing their reading position, their advancement every bit good as things that were their facets of success. During the 6 month period, she met the student twice, questioning the kids for about 30 proceedingss. Children were assured of their confidentiality in order to garner honorable responses so that school can improvize on its judicial admission in the coming academic old ages. She conducted semi-structured interviews from the students, and to assist them she used a ocular prompt based on Kelly ‘s attack ( Leadbetter et al, 1999 ) . The thought she got from both interviews was related to the public presentation e.g. both the gender expressed that they stumble when they read out loud and that they do n't wish reading in the schoolroom and maintain on spellings make them bury what to read. When the students ‘ position was asked sing the good readers, they commented that the good reader read louder and faster and that the difference between good reader and bad reader is that, the good reader makes the hapless book sound good while bad readers makes a good book sound drilling. Hence the result was that good readers have much better frequence every bit good as have much better reading. These positions of students were seen apparent even after nine months despite of the hint that their thoughts of literacy in the secondary school were developing. Few students continued to the thought that there reading is affected when they get prep and that reading is largely non the portion of their prep. Therefore the basic f eeling of kids sing reading can be confusing and if concepts of kids ‘s reading are to develop helpfully so there should be a strategic program for this. Assurance is another factor act uponing the public presentation degree Fahrenheit reading, less assurance can demo apparent diminution in their reading public presentation. Persistent and on-going encouragement of students in their reading can promote their public presentation and there are staff members holding peculiar accomplishments, they can portion the same accomplishment with other staff members to assist pupil improvise. Harmonizing to Pearson ( 2003 ) , the influential function of household is besides imperative in the procedure of reading. In her interviews with kids, she found out that students appreciated transporting out reading with person they knew although this chance was non gettable all the clip. Families promoting the privation of student to hold reading spouse might assist them in come oning and therefore will get down to hold more acute involvement in reading and may purchase books of their ain involvement for their reading calling. In footings of feedback, due to the deficiency of instruments used there were no specific standards for students ‘ thought of advancement as compared to prove consequences. However, at primary instruction phase, kids are really competent to track the advancement they made by agencies of utilizing information like coloring material they are on, the groups they are working in and or the degree with which they are asked to read. Though, this sort of system was recognized to be least available at the phase of secondary school instruction. Introducing wide stairss at the secondary degree may let the kids to track and place the advancement they have made in reading efficaciously. Hence, the critical phase for schools to ease or detain students ‘ accommodation is the period of passage that includes the Year seven. Supporting the reading procedure, hiking the assurance degrees, influential function of household and the students ‘ feedback are the most effectual ingredients in developing literacy accomplishments for both primary and secondary stage of the school.DecisionThe scope to which instruction of reading should do the stuff appropriate to be taught has been still in the Centre of statements sing reading teaching method. There is disagreement traveling on sing the all right ways to poising work on whole texts with sub-word-level work. One manner to attach man-made phonics learning firmly in an redolent context is to straight associate it to pupil ‘s books and other complete texts. The Rose Report has by now started to hold a consecutive influence on national educational policy in the United Kingdom since harmonizing to the study, the inst ructors and trainee instructors should be required to learn reading through man-made phonics foremost and fast. The interviews conducted by Sue Pearson gave two factors that encouraged the schools in order to give serious consideration to the findings, foremost was the honest responses from the students since they gave positive free of vacillation response and did n't felt forced sing their reading advancement. Second the students took engagement really earnestly. The interviews conducted gave wide scope of future considerations to schools since students shared their likes and disfavors in the reading advancement. Majority of them seem to hold better advancement in reading when they were motivated and acquiring aid from household or person they wished to read with. The information provided by the interview may play a cardinal function in the secondary school in footings of be aftering a proper focal point on the literacy and heightening student ‘s accomplishments. Hence the research of Sue Pearson consequently, discovered student ‘s aspirations, both in footings of short-run and lo ng-run every bit far as instruction of reading is concerned and will be an on-going aid for instructors in the chance.

IT Enters a New Learning Environment Essay

It is most helpful to see useful models of school learning that is ideal to achieving instructional goals through preferred application of educational technology. These are the models of Meaningful Learning, Discovery learning, Generative Learning and Constructivism. Meaningful Learning If the traditional learning environment gives stress focus to rote learning and simple memorization, meaningful learning gives focus to new experience departs from that is related to what the learners already knows. New experience departs from the learning of a sequence of words but attention to meaning. It assumes that: ââ€"  Students already have some knowledge that is relevant to new learning. ââ€"  Students are wiling to perform class work to find connections between what they already know and what they can learn. In the learning process, the learner is encouraged to recognize relevant personal experiences. A reward structure is set so that the learner will have both interest and confidence, and this incentive system sets a positive environment to learning. Facts that are subsequently assimilated are subjected to the learner’s understanding and application. In the classroom, hands-on activities are introduced so as to simulate learning in everyday living. Discovery Learning Discovery learning is differentiated from reception learning in which ideas are presented directly to student in a well-organized way, such as through a detailed set of instructions to complete an experiment task. To make a contrast, in discovery learning student from tasks to uncover what is to be learned. New ideas and new decision are generated in the learning process, regardless of the need to move on and depart from organized setoff activities previously set. In discovery learning, it is important that the student become personally engaged and not subjected by the teacher to procedures he/she is not allowed to depart from. In applying technology, the computer can present a tutorial process by which the learner is presented key concept and the rules of learning in a direct manner for receptive learning. But the computer has other uses rather than delivering tutorials. In a computer simulation process, for example, the learner himself is made to identify key concept by interacting with a responsive virtual environment. Generative Learning In generative learning, we have active learners who attend to learning events and generate meaning from this experience and draw inferences thereby creating a personal model or explanation to the new experience in the context of existing knowledge. Generative learning is viewed as different from the simple process of storing information. Motivation and responsibility are seen to be crucial to this domain of learning. The area of language comprehension offers examples of this type of generative learning activities, such as in writing paragraph summaries, developing answers and questions, drawing pictures, creating paragraph titles, organizing ideas/concepts, and others. In sum, generative learning gives emphasis to what can be done with pieces of information, not only on access to them. Constructivism In constructivism, the learner builds a personal understanding through appropriate learning activities and a good learning environment. The most accepted principles constructivism’s are: ââ€"  Learning consists in what a person can actively assemble for himself and not what he can receive passively. ââ€"  the role of learning is to help the individual live/adapt to his personal world. These two principles in turn lead to three practical implications: ââ€"  the learner is directly responsible for learning. He creates personal understanding and transforms information into knowledge. The teacher plays an indirect role by modeling effective learning, assisting, facilitating and encouraging learners. ââ€"  the context of meaningful learning consists in the learner â€Å"connecting† his school activity with real life. ââ€"  the purpose of education is the acquisition of practical and personal knowledge, not abstract or universal truths. To review, there are common t hemes to these four learning domains. They are given below: Learners ââ€"  are active, purposeful learners. ââ€"  set personal goals and strategies to achieve these goals. ââ€"  make their learning experience meaningful and relevant to their lives. ââ€"  seek to build an understanding of their personal worlds so they can work/live productively. ââ€"  build on what they already know in order to interpret and respond to new experiences. LB#6: IT Enters a New Learning Environment. Effective teachers best interact with students in innovative learning activities, while integrating technology to the teaching-learning process. In Meaningful learning * Students already have some knowledge that is relevant to new learning * Students are willing to perform class work to find connections between what they already know and what they can learn. In Discovery learning Ideas are presented directly to students in a well-organized way, such as through a detailed set of instructions to complete an experiment or task. In applying technology, the computer can preset a tutorial process by which the learner is presented key concepts and the rules of learning in a direct manner for receptive learning. In Generative Learning Active learners who attend to learning events and generate meaning from this experience and draw inferences thereby creating a personal model or explanation to the new experience in the context of existing knowledge.Motivation and responsibility are seen to be crucial to this domain of learning. In Constructivism The learner builders a personal understanding through appropriate learning activities and a good learning environment. Learners: are active, purposeful learners. Set personal goals and strategies to achieve these goals. Make their learning experience meaningful and relevant to their lives. Seek to build an understanding of their personal worlds so they can work/live productively. Build on what they already know in order to interpret and respond to new experiences.

Martin Luther King in campaigning in the North Essay

In 1966 Martin Luther King decided to focus on dealing with the problems in the North particularly Chicago. The problems that he encountered here were very different to those that he had had so much success with in the South. Dealing with the economic and social segregation that he faced here proved difficult for several reasons. The problems facing blacks in the North, stemmed from a variety of different areas including education, employment, housing etc. Although King was able to identify the problems being faced in these areas, particularly housing, he still largely relayed on the same tactics that he and the Southern Christian Leadership Conference (SCLC) had used in the South. However, the mayor of Chicago (Daley) would avoid making a hostile response such as that of ‘Bull’ Connor in Birmingham. The authorities here were more subtle to avoid gaining the attention of the media e.g. the police would avoid using brutality and Daley even blamed violence for social decay*. This prevented the movement from gaining as much publicity and support as in previous years. King also tried to come to some sort of agreement with Daley regarding housing. However, Daley was reluctant to do so fearing the loss of votes of the white working class. Actions such as this added to the anger that blacks in Chicago felt towards the white authorities and increased their unwillingness to co-operate. Both Mayor Daley’s refusal to help and King’s disorganisation when planning the Chicago campaign played an important role in its failure. Chicago suffered more from problems in racial division than other cities in the North, and so perhaps it was not a good starting point for the campaign here. Locals would sometimes blame blacks for inciting race riots and these divisions were illustrated by the marches organised by the SCLC in 1966, which ended in violence from mobs. * * In Chicago most blacks lived in ghettoes to the south of the city. Therefore it appears reasonable that these people often found it difficult to relate to Martin Luther King and his middle class background. The SCLC had never had much grass roots support unlike other organisations, such as the Student Non-violent Coordinating Committee (SNNC). Although in the South this had  still allowed them to have success, in Chicago most blacks were working class and looking for improvements in housing, less poverty and some overall change brought about by an end to de facto segregation. However, in the South the need for change had been more political- an end to de jure segregation. Given these differences, many northern blacks felt that King’s non-violent philosophy did not represent their views. It would be difficult to change these attitudes – here, perhaps as a result of poverty, the amount of gang warfare and crime was much higher than in the South. Change would undoubtedly take time- more than the few months that the SCLC had planned for the campaign to last. There were quite clear social divisions between black communities in the South and North. One of the most important examples of this is that the churches in the North were not as successful at organising their community as churches in the South had been. This was partly due to a lack of co-operation, and partly due to the fact that the Christian faith was much stronger in the South. It was at this point that many blacks were beginning to join alternative ‘black power’ groups. Overall it appears that King underestimated the differences between the North and the South and the divisions that were evident amongst the black community. He was unfamiliar with the attitudes of those in the North and did not make an accurate assessment of the situation. As a result of this the tactics employed by the SCLC were not as successful as originally hoped. * http://www.revision-notes.co.uk/revision/59.html ** http://www.reportingcivilrights.org/