A Critique of Sir Anthony Hopkins's Oscar winning performance of Essay

A Critique of Sir Anthony Hopkins's Oscar winning performance of Hannibal Lecter in 'The Silence of The Lambs' - Essay Example 3 Hannibal was by all accounts an evil, savage, cannibal and serial killer and yet he became an enthralling, and often sympathetic character.4 The fact that a psychiatrist who murders and then eats his patients could become such a captivating character is puzzling.When Hopkins meets the FBI agent for the first time,I could not help thinking that he while he seemed to be toying with her,he had some degree of respect and suspicion in his interaction with her and Hopkins gave these impressions through body language and inflection.At one point he winked at her as if to share a private joke and at another point,he smiled at her,giving the impression that he had normal feelings.In my opinion, Hopkins brought a very complex character to life in a realistic and convincing way.Lecter was â€Å"psychopathic personality evidenced by his superficial charm,manipulativeness and lack of remorse of empathy for the victims†.For example, when the agent interviewing Lecter mentioned that serial killers usually kept a relic of their victims, Lecter interjected, â€Å"I didn’t† in a matter of fact way. ... After all, Lecter was previously portrayed by actor Brian Cross and introduced him to the screen, it was Hopkins’ Lecter that made the greatest impression and immortalized him.7 Hannibal Lecter was a brutal cannibal and there was nothing sympathetic about the character and it was not intended that Lecter was sympathetic. Lecter is â€Å"revoltingly evil, a characterization brought chillingly to life for millions by Anthony Hopkins† in Silence of the Lamb.8 For example when discussing the murder of a former patient, Lecter denies killing his patient but states without emotion that it was best for him â€Å"as his therapy was going nowhere anyway†. However, Barr explains that Hopkins was brilliant in how he chose to play Lecter.9 Hopkins portrayed an ordinary man who did extraordinary deeds and came across as a man you would not run away from if you happened to come across him on the streets. I observed this in his demeanour as he urged the FBI agent in training to show her credentials and when she did, he urged her to come closer, as if he did not think she should fear a man like him. In other words, Hopkins played Lecter as though, Lecter thought of himself as normal and this brought both sympathy to the character and made him likable and evil all at the same time. One acting technique that Hopkins brought to the character of Lecter was his refusal to judge the character. As Hopkins himself said: †¦As an actor, I can’t judge because moral judgement gets in the way of the characterization. If you start doing that, you end up playing the character like a zombie or a vauderville villain.10 In other words, Hopkins took and inside-out approach to his portrayal of Lecter. He portrayed the character by

Citibank Budgeting Essay Example for Free

Citibank Budgeting Essay 1. Analysis of Budget Process at Citibank Direction and control of Citibank’s international branches are conducted via two formal management processes. Each year, top management sets sovereign risk limits for its independent branches based on proposals by country managers. Country managers may choose to operate with self-imposed limits below this upper guideline. Following, there is the budget setting process, where headquarters only provides administrative guidelines but not specific targets, with operating managers being responsible for budgets for the following year. Indonesia often set their targets above these long-term goals. Performance is measured and compared against the budget each month, and a new forecast, which will be reviewed by the division manager, is drawn up each quarter for the remainder of the year. This structure of bottom-up budgeting is appropriate for a decentralized firm like Citibank. This is evident from the freedom Mr Mistri has over Indonesia’s operations and the different business segments and divisions, as shown in Exhibit 2 3. Such a participative process is likely to increase management commitment to achieve the targets since country managers are responsible for influencing their own targets. More importantly, country managers know the local business environment and culture better than group managers, therefore their targets are likely to be more accurate and realistic. Furthermore, bottom-up budgeting is a form of action control while the frequent reviews of the budgets serves as preaction review. Both facilitate information sharing within the organisation, with long-term strategic goals of the firm being communicated downwards and local business potentials and risks conveyed upwards via the budgets and forecasts. They also encourage managers to think further ahead about what they want to achieve in the near future. However, Citibank’s budgeting process appears to have an imbalanced focus with most of the emphasis placed only on financial measures. Although these measures can be easily obtained and are inexpensive because they are by-products of the accounting system, it does not fully represent all aspects of the organization’s strategies and goals. Instead, the budget could be restructured to include other non-financial aspects such as customer satisfaction and employee morale to obtain a balance, which will be vital towards the long term success of Citibank. None of Citibank’s budget items extend beyond the next year; instead there is an emphasis on a fixed short horizon. This can result in managers developing a myopic focus instead of measuring the fulfilment of Citibank’s long-term goals or the local government’s societal expectations. Myopia is aggravated by the monthly performance reviews which reveals the focus on short term goals. Citibank should look at its budget with a longer horizon. Moreover, the budgeting process in Citibank appears to be tedious and too time-consuming. The requirement for operating managers to conduct discussions and forecast all the line items shown on the submission form seems to take up significant time and effort. Such a process could be costly for the firm in terms of opportunity costs related to unnecessary time and resources spent. The benefits of such a tedious budgeting process must be high enough to justify the related costs. Also, there seems to be no connection and a mismatch between the two management processes. It is only reasonable that these two processes should go hand in hand as higher returns may only be possible with a higher risk appetite. However, increase in profit goals is not matched by an increase in risk tolerance (sovereign risk limit). Moreover, sovereign risk limits is set yearly but not adjusted when budgets are revised each quarter. Citibank should consider allowing country/division managers to adjust risk limits to match any revisions in budgets during the year. Use of the Budget for Performance Evaluation of Managers Performance is monitored every month against budgets, and incentive compensation for managers were linked to budget-related performance. Incentive compensation could range up to approximately 70% of base salary although awards of 30-35% were more typical. Assignment of bonuses were based approximately 30% on corporate performance and 70% on individual performance, primarily performance related to forecast. This emphasizes results accountability as it involves rewarding the managers for generating good results that are aligned to the budgets. As such, it influences actions because it causes employees to be concerned about the consequences of the actions they take. However, the contradiction in this is that while these managers will not be constrained in what actions they can take to achieve their goals, they are also empowered to take whatever actions they believe will best produce these desired results. Hence, it is highly dependent on personnel controls with regards to the managers hired. Provided that budgets were adequately set with appropriately extent of goal difficulty, these budgets act as results controls and affects a manager’s motivation since the targets are linked to performance evaluations and compensation. Furthermore, it is beneficial that manager’s compensation is tied directly to both individual(70%) and corporate performance(30%), allowing a larger perspective to be considered. Differentiation of base earnings from extraordinary earnings for which managers are not held accountable for is in line with the controllability principle. This is vital because setting performance targets to attain for each measure allows the managers to assess their performance and also get rewarded, encouraging behaviors that lead to desired results. As such, this will promise manager rewards that provide the most powerful motivational effects in the most cost effective ways possible. However, performance evaluations based on budgeted information is backward looking (extrapolating past trends) while it is best that the evaluations be forward looking. It should be evaluated based on the future cash flow/profits that can be brought to the firm instead of historical performance to promote a higher performance in the future. Furthermore, as their compensation is tied to meeting targets, it might promote game-playing and politics. As for Citibank, their culture encourages aggressive mark-ups to budget with managers constantly setting challenging budgets. For Mr Mistri, he will feel the extra pressure since the aggressive targets can barely be met with the deteriorating conditions in Indonesia. In conclusion, the current budget and performance evaluation system, which is mainly bottom up with top down guidance, matches the decentralized structure of Citibank. Although there seems to be trade-offs and problems with Citibank’s current bottom up budgeting system, there is no perfect budget system to optimally serve all the different purposes of budgeting. What Citibank can do is to put in place measures to minimize some of these shortcomings. 2. Are managers at Citibank committed to achieving budget targets? Yes, managers are committed as a result of their freedom to set their own budgets subject to guidelines provided by top management. Mr Mistri can choose to operate with a self imposed sovereign risk limit which is lower than the one approved by the New York Headquarter if he thinks that the one set by the top management is too aggressive. The fact that he has control over this means that he will be less pressurised to set unrealistic goals or engage in budget slacks. These will garner higher commitment from managers since the budgets set are not restrictive and offer flexibility to managers according to the business conditions. The commitment to achieve targets is augmented as incentive compensation for managers is linked to budget-related performance. Thus they will work towards getting more incentives for themselves through surpassing the budgeted forecast. On the other hand, the amount of commitment may be limited by the constant revision of budgets each quarter. Managers may be less motivated to hit their budget target if they know that those targets can be revised lower in the next quarter if performance was unsatisfactory. In addition, the frequent changes may make managers unfocused and reduce their motivation to work towards the goals set. If so, are the budget targets too challenging? The targets may prove to be too challenging. This can be shown by the fact that although incentive compensation could range up to 70% of base salary, awards of 30-35% were more typical, implying that it may be difficult to surpass the budgeted levels. Furthermore, Mr Mistri felt that the increased profit goal by $500K to $1mil set by Mr Gibson is too much as the budget he submitted is already very aggressive, judging by the bleak short term outlook due to the decrease in oil prices. This is supported by the self-imposed sovereign risk limit that Mr Mistri is operating at in order to minimise his exposure which will reduce the likelihood of the firm achieving higher returns due to the lower risk. We also doubt the achievability of the budget set. Though the forecasts and budgets are set by the operating managers themselves, we have to take into consideration Citibank’s risk-taking culture. While challenging targets induce motivation, the aggressive year-on-year increase in targets might prove to be detrimental to the achievement of the firm’s strategic objectives. Firstly, this is especially so when targets are not adjusted in times of bad macroeconomic conditions. With the incentive compensation for managers linked to budget-related performance, it seems that managers might be motivated to set unrealistic targets and employ excessively risky methods to accomplish them. Such a system would eventually serve to promote short-term gains at the expense of long-term losses for the organisation. In addition, the practice of comparing actual performance to budgeted amount monthly is too short termed and may render managers to become myopic. This may encourage managers to engage in gamesmanship such as earnings management, manipulating data to receive additional bonuses especially towards the end of a quarter. Is there any evidence of budget gaming? Yes, there is evidence of budget gaming. The main reason Mr Mistri used to justify his less than ideal budget (as compared to Mr Gibson’s) is likely to be false and there are unhealthy motivations for him to engage in such behaviour. Mr Mistri justified his budget by claiming that the Indonesian economy had slipped into a recession when oil prices decreased significantly. This is supported by the fact that Citibank’s Indonesian operations growth paralleled that of the Indonesian economy. However, evidence in Exhibit 4 suggests otherwise, revealing both the net and inflation-adjusted GDP of Indonesia to be increasing steadily over time. Even in 1983, GDP increased 5%. Moreover, a fall in oil prices will not necessarily lead to a recession. As such, Mr Mistri’s concerns are unlikely to be true and a simple check by the group managers would have allowed this to be uncovered. Moreover, even though Indonesia’s economy is highly dependent on oil prices, a fall in oil prices is also likely to affect Citibank’s other operations in different regions. The group managers will probably have considered this effect when setting the $4mil profit goal for South East Asia. In our opinion, Mr Mistri is likely to be acting as a Sandbagger. By presenting less ambitious budgets, there will be higher likelihood of positive variances in actual performance. Given that compensation is tied to budget-related performance, such gaming behavior will probably increase Mr Mistri’s bonuses and salary. Another motive for budget gaming could be to cover up the ongoing high staff turnover problem with the bad economic conditions. Mr Mistri just lost his chief of staff and two senior officers, and is concerned with constraints to growth due to his lack of experienced staff. This could in turn affect his bonus and salary. Since managers are not accountable for extraordinary earnings or losses at Citibank, by blaming the external economy (recession) for a less aggressive budget rather than on internal problems, his bonuses will not be affected. Furthermore, Mr Minstri has the freedom to operate at a sovereign risk limit lower than the group’s since country managers are given substantial autonomy in deciding their country’s budget and risk limits. He is likely to be able to get away with a less than optimal budget if his group manager trusts him. This way, his budget gaming behavior will escape the suspicions of Mr Gibson and other group/division managers.

The Net Domestic Products (NDP) Equals The Gross Dmestic Product (GDP) Essay Example for Free

The Net Domestic Products (NDP) Equals The Gross Dmestic Product (GDP) Essay The net domestic product (NDP) equals the gross domestic product (GDP) minus depreciation on a countrys capital goods. Net domestic product accounts for capital that has been consumed over the year in the form of housing, vehicle, or machinery deterioration. The depreciation accounted for is often referred to as capital consumption allowance and represents the amount of capital that would be needed to replace those depreciated assets. If the country is not able to replace the capital stock lost through depreciation, then GDP will fall. In addition, a growing gap between GDP and NDP indicates increasing obsolescence of capital goods, while a narrowing gap means that the condition of capital stock in the country is improving. Gross domestic product (GDP) is the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP per capita is often considered an indicator of a countrys standard of living;[2][3] GDP per capita is not a measure of personal income (See Standard of living and GDP). Under economic theory, GDP per capita exactly equals the gross domestic income (GDI) per capita (See Gross domestic income). GDP is related to national accounts, a subject in macroeconomics. GDP is not to be confused with gross national product (GNP) which allocates production based on ownership. The University Grants Commission (UGC) of India is a statutory organisation set up by Union government in 1956, for the coordination, determination and maintenance of standards of university education. It provides recognition for universities in India, and provides funds for government-recognised universities and colleges. Prof. Ved Prakash, a noted academician and education administrator, is the Chairman of UGC, India. Its headquarters are in New Delhi, and six regional centres in Pune, Bhopal, Kolkata, Hyderabad, Guwahati and Bangalore.

The Reinsurance Expected Loss Cost Formula

The Reinsurance Expected Loss Cost Formula ELCF is the excess loss cost factor (as a percentage of total lost cost). PCP is the primary company/subject premium. PCPLR is the primary company permissible loss ratio (including any loss adjustment expenses covered as a part of loss) RCF is the rate correction factor which is the reinsurers adjustment for the estimated adequacy or inadequacy of the primary rate Given that the coverage of this treaty is per-occurrence, we must also weigh the manual difference rate for the clash exposure. In order to determine the reinsurers excess share the ALAE is added to each claim, and therefore claims from policy limits which are below the attachment point will be introduced into the excess layer. The reinsure may have own data that describe the bi-variate distribution of indemnity and ALAE, or such information can be obtained from ISO or similar organization outside of United States of America. With these data the reinsurer is able to construct the increased limits tables with ALAE added to the loss instead of residing in its entirety in the basic limits coverage. Another more simple alternative is to adjust the manual increased limits factors so that they to account for the addition of the ALAE to the loss. A basic way of doing this is to use the assumption that the ALAE for each and every claim is a deterministic function of indemnity amount for the claim, which means adding exactly ÃŽÂ ³% to each claim value for the range of claim sizes that are near the layer of interest. This ÃŽÂ ³ factor is smaller than the overall ratio of ALAE to ground-up indemnity loss, as much of the total ALAE relates to small claims or claims closed with no indemnity. Assumption: when ALAE is added to loss, every claim with indemnity greater than $300,000 = (1+ ÃŽÂ ³) enters the layer $1,400,000 excess of $600,000, and that the loss amount in the layer reaches $1,400,000 when the ground-up indemnity reaches $2,000,000 = (1+ ÃŽÂ ³). From this the standard increased limits factors can be modified to account for ALAE added to the loss. In this liability context, Formula for RELC can be used with PCP as the basic limit premium and PCPLR can be used as the primary company permissible basic limits loss ratio. Assumption: Given the clash exposure an overall loss loading of ÃŽÂ ´% is sufficient enough to adjust the loss cost for this layer predicted from the stand-alone policies. Then ELCF determines the excess loss in the layer $1,400,000 with excess of $600,000 which arises from each policy limit and plus its contribution to the clash losses as a percentage of the basic limits loss that arise from the same policy limit. The formula for ELCF which is evaluated at limit (Lim) is as follows: Formula : Liability ELCF for ALAE Added to Indemnity Loss ELCF(Lim) = 0 Where Attachment Point AP = $600,000 Reinsurance Limit RLim = $1,400,000 Clash loading ÃŽÂ ´ = 5% Excess ALAE loading ÃŽÂ ³ = 20% The table 2 displays this method for a part of Allstates exposure using the hypothetical increased limits factors to calculate the excess loss cost factors with both ALAE and risk load excluded. Table 2: Excess Loss Cost Factors with ALAE Added to Indemnity Loss at 20% add-on and a Clash Loading of 5% Table : Excess Loss Cost Factors with ALAE Added to Indemnity Loss at 20% add-on and a Clash Loading of 5% (1) Policy Limit in $ (2) ILF w/o risk load and w/o ALAE (3) ELCF 200,000 1.0000 0 500,000 1.2486 0 600,000 1.2942 0.0575 1,000,000 1.4094 0.2026 1,666,666 1.5273 0.3512 2,000,000 or more 1.5687 0.4033 Source: own calculation based on Patrik (2001) Using the Formula 4., the ELCF($600,000) = 1.20*1.05*(1.2942-1.2486) = 0.0575, and ELCF($2,000,000) =1.20*1.05*(1.5687-1.2486) = 0.4033. Assumption1: for this exposure the Allstates permissible basic limit loss ratio is PCPLR = 70%. Assumption2: reinsurers evaluation indicates that the cedants rates and offsets are sufficient and therefore RCF is 1.00. The reinsurer can now calculate the exposure rate RELC and the reinsurers undiscounted estimate of loss cost in the excess layer as can be seen in the table 3. Table 3: Reinsurance Expected Loss Cost (undiscounted) Table : Reinsurance Expected Loss Cost (undiscounted) (1) Policy Limit in $ (2) Estimated Subject Premium Year 2009 in $ (3) Manual ILF (4) Estimated Basic Limit Loss Cost 0.70x(2)/(3) (5) ELCF (6) RELC in $ (4)x(5) Below 600,000 2,000,000 1.10 (avg.) 1272727.27 0 0 600,000 2,000,000 1.35 1,037,037.04 0.0575 59,629.63 1,000,000 2,000,000 1.50 933,333.33 0.2026 189,093.33 2,000,000 or more 4,000,000 1.75 (avg.) 1,600,000.00 0.3512 562,920.00 Total 10,000,000 n.a. 4,843,197.64 n.a. 811,642.96 Source: own calculation based on Patrik (2001) An exposure loss cost can be estimated using probability models of the claim size distributions. This directly gives the reinsurer the claim count and the claim severity information which the reinsurer can use in the simple risk theoretic model for the aggregate loss. Assumption: the indemnity loss distribution underlying Table 2 is Pareto with q =1.1 and b =5,000. Then the simple model of adding the 20% ALAE to the indemnity per-occurrence changes the indemnity of a Pareto distribution to a new Pareto with q =1.1and b=5,000*1.20 = 6,000. The reinsurer has to adjust the layer severity for a clash and this can be done by multiplying with 1+ÃŽÂ ´ =1.05. The reinsurer can therefore calculate from each policy limit the excess expected claim sizes, after dividing the expected claim size by the RELC for each limit the reinsurer obtains the estimates of expected claim count. This is done in Table 4. The expected claim size can be calculated as follows: Firstly the expected excess claim severity over the attachment point d and subject to the reinsurance limit RLim for a policy limit ÃŽÂ » can has to be calculated. This can be done as follows: For ÃŽÂ »= 600,000 For ÃŽÂ »=1,000,000 For ÃŽÂ »=2,000,000 The reinsurer is now able to calculate the expected claim count, the estimation can be seen in the table 4: Table 4: Excess Expected Loss, Claim Severity and Claim Count Table : Excess Expected Loss, Claim Severity and Claim Count Policy Count in $ (2) RELC in $ (3) Expected Claim Size in $ (4) Expected Claim Count (2)/(3) 600,000 59,629.63 113,928 0.523 1,000,000 189,093.33 423,164 0.447 2,000,000 or more 562,920.00 819,557 0.687 Total 811,642.96 1,356,649 1.68 Source: own calculation based on Patrik (2001) The total excess expected claim size for this exposure is $1,356,649. If the independence of claim events across all of the exposures can be assumed, the reinsurer can also obtain total estimates of the overall excess expected occurrence (claim) size and the expected occurrence (claim) count. Now we are going to estimate the experience rating. Step 3: Gather and reconcile primary claims data segregated by major rating class groups. As in the Example of property quota share treaties, the reinsurer needs the claims data separated as the exposure data, and the reinsurer also wants some history of the individual large claims. The reinsurer usually receives information on all claims which are greater than one-half of the proposed attachment point, but it is important to receive as much data as possible. Assumption: a claims review has been performed and the reinsurer received a detailed history for each known claim larger than $100,000 occurring 2000-2010, which were evaluated 12/31/00, 12/31/01à ¢Ã¢â€š ¬Ã‚ ¦, 12/31/09, and 6/30/10. Step 4: Filter the major catastrophic claims out of the claims data. The reinsurer wants to identify clash claims and the mass tort claims which are significant. By separating out the clash claims, the reinsurer can estimate their size and their frequency and how they relate to the non-clash claims. These statistics should be compared to the values that the reinsurer knows from other cedants and therefore is able to get a better approximation for the ÃŽÂ ´ loading. Step 5: Trend the claims data to the rating period. As with the example for the property-quota share treaties, the trending should be for the inflation and also for other changes in the exposure (e.g. higher policy limits) which may affect the loss potential, but unlike with the proportional coverage, this step cannot be skipped. The reason for this is the leveraged effect which has the inflation upon the excess claims. The constant inflation rate increases the aggregate loss beyond any attachment point and it increases faster than the aggregate loss below, as the claims grow into the excess layer, whereas their value below is stopped at the attachment point. Each ground-up claim value is trended at each evaluation, including ALAE, from year of occurrence to 2011. For example, consider the treatment of a 2003 claim in the table 5. Table 5: Trending an Accident Year 2003 Claim Table : Trending an Accident Year 2003 Claim (1) Evaluation Date (2) Value at Evaluation In $ (3) Trend factor (4) 2011 Level Value in 4 (5) Excess Amount in$ 12/31/03 0 1.62 0 0 12/31/04 0 1.62 0 0 12/31/05 250,000 1.62 405,000 0 12/31/06 250,000 1.62 405,000 0 12/31/07 300,000 1.62 486,000 0 12/31/08 400,000 1.62 648,000 48,000 12/31/09 400,000 1.62 648,000 48,000 06/30/10 400,000 1.62 648,000 48,000 Source: own calculation based on Patrik (2001) The reasoning for a single trend factor in this example is that the trend affects the claim values according to the accident date and not by an evaluation date. The trending of the policy limits is a delicate issue, because if a 2003 claim on a policy which has limit that is less than $500,000 inflates to above $600,000 ( plus ALAE), will be the policy limit that will be sold in the year 2011 greater than $500,000? It seems that over long periods of time, that the policy limits change with inflation. Therefore the reinsurer should over time, if possible, receive information on the Allstates policy limit distributions. Step 6: Develop the claims data to settlement values. The next step is to construct the historical accident year, thus we want to develop the year triangles for each type of a large claim from the data which was produced in column (5) of Table 5. Typically all claims should be combined together by major line of business. Afterwards the loss development factors should be estimated and applied on the excess claims data while using the standard methods. Also in order to check for reasonableness and comparable coverages we want to compare the development patterns that were estimated from Allstates data to our own expectations which have their basis in our own historical data. When considering the claim in Table 5 we see that only $48,000 is over the attachment point, and also only at the fifth development point Table 6: Trended Historical Claims in the Layer $1,400,000 Excess of $600,000 (in $1,000s) Table : Trended Historical Claims in the Layer $1,400,000 Excess of $600,000 (in $1,000s) Assumption: our triangle looks like the Table 6: Acc. Year Age 1 in $ Age 2 in $ Age 3 in $ à ¢Ã¢â€š ¬Ã‚ ¦ Age 9 in $ Age 10 in $ Age 10.5 in $ 2000 0 90 264 à ¢Ã¢â€š ¬Ã‚ ¦ 259 351 351 2001 0 0 154 à ¢Ã¢â€š ¬Ã‚ ¦ 763 798 à ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¦ 2008 77 117 256 2009 0 0 2010 0 ATA 4.336 1.573 1.166 à ¢Ã¢â€š ¬Ã‚ ¦ 1,349 n.a. n.a. ATU 15.036 3.547 2.345 à ¢Ã¢â€š ¬Ã‚ ¦ 1.401 1.050 = tail Smoothed Lags 11.9% 28.7% 47.7% à ¢Ã¢â€š ¬Ã‚ ¦ 93.1% 95.3% 96.7% Source: own calculation based on Patrik (2001) Where: ATA is Age-To-Age development factor ATU is Age-To-Ultimate development factor Lag(t) is the percentage of loss reported at time t The selection of the tail factor of 1.05 is based upon the general information about the development for this type of an exposure beyond ten years. By changing to the inverse for the point of view from the age-to-ultimate factors, the time lags of the claim dollar reporting, the loss reporting view is transformed to that of the cumulative distribution function (CDF) whose domain is [0,), this transformation gives a better outlook of the loss development pattern. It also allows considering and measuring the average (expected) lag and some other moments, that are comparable to the moments of loss development patterns from other exposures. Given the chaotic development of excess claims, it is a important to employ smoothing technique. If the smoothed factors are correctly estimated they should more credible loss development estimates which are more credible. They also allow to evaluate the function Lag( ) at every positive time. The smoothing which was introduced in the last row of Table 6 is based on a Gamma distribution with a mean of 4 (years) and a standard deviation of 3. It is also usually useful to analyze the large claim paid data, if possible, both to estimate the patterns of the excess claims payment and also to supplement the ultimate estimates which are based only on the reported claims that were used above. Sometimes the only data available are the data on aggregate excess claims, which would be the historical accident year per development year $1,400,000 excess of $600,000 aggregate loss triangle. Pricing without specific information about the large claims in such a situation, is very risky, but it is occasionally done. Step 7: Estimate the catastrophic loss potential. The mass tort claims such as pollution clean-up claims distort the historical data and therefore need special treatment. As with the property coverage, the analysis of Allstates exposures may allow us to predict some suitable loading for the future mass tort claim potential. As was said in the Step 4, the reinsurer needs to identify the clash claims. With the separation of the clash claims, for each claim, the various parts are then added together to be applied to the occurrence loss amount at the attachment point and at the reinsurance limit. If it is not possible to identify the clash claims, then the estimation of the experience of RELC has to include a clash loading which is based on judgment of the general type of exposure. Step 8: Adjust the historical exposures to the rating period. As in the example on the property quota-share treaties the historical exposure (premium) data has to be adjusted in such a manner that makes the data are reasonably relevant to the rating period, therefore the trending should be for the primary rate, for the underwriting changes and also for other changes in exposure that may affect the loss potential of the treaty.. Step 9: Estimate an experience expected loss cost, PVRELC, and, if desirable, a loss cost rate, PVRELC/PCP. Assumption: we have trended and developed excess losses for all classes of Allstates casualty exposure. The standard practice is to add the pieces up as seen in the table 7. Table 7: Allstate Insurance Company Casualty Business Table : Allstate Insurance Company Casualty Business (1) Accident Year (2) Onlevel PCP in $ (3) Trended and Developed Loss and Excess Loss (estimated RELC) in $ (4) Estimated Cost Rate in % (3)/(2) 2002 171,694 6,714 3.91 2003 175,906 9,288 5.28 2004 178,152 13,522 7.59 2005 185.894 10,820 5.82 2006 188,344 9,134 4.58 2007 191,348 6,658 3.48 2008 197122 8,536 4.33 2009 198,452 12,840 6.47 2010 99,500 2,826 2.84 Total 1,586,412 80,336 5.06 Total w/o 2010 1,486,912 77,510 5.21 Source: own calculation based on Patrik (2001) The average loss cost rate for eight years is 5.21%, where the data from the year 2010 was eliminated as it is too green (undeveloped) and there does not seem to be a particular trend from year to year. Table 7 gives us the experience-based estimate, RELC=PCP =5.21%, but this estimate has to be loaded for the existing mass tort exposure, and also for the clash claims if we had insufficient information on the clash claims in the claims data. Step 10: Estimate a credibility loss cost or loss cost rate from the exposure and experience loss costs or loss cost rates The experience loss cost rate has to be weighed against the exposure loss cost rate that we already calculated. If there is more than one answer with different various answers that cannot be further reconciled, the final answers for the $1.400, 000 excess of $600,000 claim count and for the severity may be based on the credibility balancing of these separate estimates. All the differences should however not be ignored, but should be included in the estimates of the parameter (and model) uncertainty, and therefore providing a rise to a more realistic measures of the variances, etc., and of the risk. Assumption: simple situation, where there are weighed together only the experience loss cost estimate and the exposure loss cost estimate. The six considerations for deciding on how much weight should be given to the exposure loss cost estimate are: The accuracy of the estimate of RCF, the primary rate correction factor, and thus the accuracy of the primary expected loss cost or loss ratio The accuracy of the predicted distribution of subject premium by line of business For excess coverage, the accuracy of the predicted distribution of subject premium by increased limits table for liability, by state for workers compensation, or by type of insured for property, within a line of business For excess coverage, the accuracy of the predicted distribution of subject premium by policy limit within increased limits table for liability, by hazard group for workers compensation, by amount insured for property For excess coverage, the accuracy of the excess loss cost factors for coverage above the attachment point For excess coverage, the degree of potential exposure not contemplated by the excess loss cost factors The credibility of the exposure loss cost estimation decreases if there are problems with any of these six items listed. Also the six considerations from which can be decided how much weight can be given to the experience loss cost estimate are: The accuracy of the estimates of claims cost inflation The accuracy of the estimates of loss development The accuracy of the subject premium on-level factors The stability of the loss cost, or loss cost rate, over time The possibility of changes in the underlying exposure over time For excess coverage, the possibility of changes in the distribution of policy limits over time The credibility of the experience loss cost estimate lessens with problems with any of the six items. Assumption: the credibility loss cost rate is RELC/PCP = 5.75%. For each of the exposure category a loss discount factor is estimated, which is based on the expected loss payment pattern for the exposure in the layer $1,400,000 excess of $600,000, and on a chosen investment yield. Most actuaries support the use of a risk-free yield, such as U.S. Treasuries for U.S. business, for the approximation of the maturity of the average claim payment lag. Discounting is significant only for longer tail business. On a practical base for a bond maturity which is between five to ten years it is better to use a single, constant fixed rate. Assumption: the overall discount factor for the loss cost rate of 5.75% is RDF= 75%, which gives PVRELC/PCP = RDF*RELC/PCP =0.75*5.75%= 4.31%, or PVRELC= 4.31% * $200,000,000 = $8,620,000. The steps 11 and 12 with this example are reversed. Step 12: Specify values for RCR, RIXL, and RTER Assumption: the standard guidelines for this size and type of a contract and this type of an exposure specify RIXL = 5% and RTER = 15%. The reinsurance pure premium RPP can be calculated as RPP = PVRLC/(1-RTER) = $8,620,000/0.85 = $10,141,176 with an expected profit as RPP PVRELC = $10,141,176 $8,620,000 = $1,521,176 for the risk transfer. As the RCR = 0% we can calculate the technical reinsurance premium of RP = RPP/(1-RIXL) = $10,141,176 /0.95 = $10,674,922. This technical premium is therefore above the maximum of $10,000,000 which was specified by the Allstate Insurance Company. If there is nothing wrong with technical calculations, then the reinsurer has two options. The first one is to accept the expected reinsurance premium of $10,000,000 at a rate of 5%, with the expected profit reduced to $10,000,000 $8,620,000 = $1,380,000 Or secondly the reinsurer can propose a variable rate contract, with the reinsurance rate varying due to the reinsurance loss experience, which in this case is a retrospectively rated contract. As the Allstate Insurance Company is asking for a retrospectively rated contract we select the second possibility. To construct a fair and balanced rating plan, the distribution of the reinsurance of an aggregate loss has to be estimated. Now we proceed with step 11. Step 11: Estimate the probability distribution of the aggregate reinsurance loss if desirable, and perhaps other distributions such as for claims payment timing. In this step the Gamma distribution approximation will be used. As our example is lower (excess) claim frequency situation, the standard risk theoretic model for aggregate losses will be used together with the first two moments of the claim count and the claim severity distributions to approximate the distribution of aggregate reinsurance loss. The aggregate loss in the standard model is written as the sum of the individual claims, as follows. Formula : Aggregate Loss L=X1 + X2 +à ¢Ã¢â€š ¬Ã‚ ¦+ XN with L as a random variable (rv) for aggregate loss N as a rv for number of claims (events, occurrences) Xi as rv for the dollar size of the ith claim The N and Xi are referring to the amount of the ith claim and to the excess number of claims. To see how the standard risk theoretic model relates to the distributions of L, N and the Xis see Patrik (2001). We are working with the assumption that the Xis are both identically and independently distributed and also independent of N, further we assume that the kth moment of L is determined completely by the first k moments of N and the Xis. There is following relationships. Formula : First Two Central Moments of the Distribution of Aggregate Loss under the Standard Risk Theoretic Model E[L] = E[N] x E[X] Var[L] = E[N] x E[X2] + (Var[N] E[N]) x E[X]2 Assumption: the E[L] = RELC =5.75%*$200,000,000 = $11,500,000 (undiscounted). We assume simplistically independent and identical distribution of the excess claim sizes and also the independency of the excess claim (occurrence) count. Usually this is a reasonable assumption. For our layer $1,400,000 excess of $600,000, our modeling assumptions and results are shown in the formula below. Formula : Allstate $1,400,000 Excess of $600,000 Aggregate Loss Modeling Assumptions and Results

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ESSAY John Steinbeck novel, ‘Of Mice and Men’, contains three characters who could be described as social misfits. In this essay I am going to describe Candy, Crooks, and Curley’s wife and examine what Steinbeck is attempting to tell the reader about the lives and situation of each of these characters. The book, ‘Of Mice and Men’, was set in the depression of 1930s in California in a place called Soledad. ‘Of Mice and Men’, can be viewed as a compassionate story of John Steinbeck for the hard life, poor, old age ,unskilled workers displaced by the depression a bad economic and high unemployment with no hope and no future. They had to leave their families and homes just to make money. Candy is such a character. He is the bunkhouse cleaner and he is old and has no family and the only old man on the ranch. We are told him in the book, that he is a good natured old gossip. He is a useful source of information about all the character at the ranch. We know because when George and Lennie arrive in to the ranch, Candy tells them about all the characters and also tells them about what is going on in the bunkhouse, for example when he says to George and Lennie about Curley, â€Å"Curley like a lot of little guys. He hates big guys†¦.† So first of all they know that how aggressive and bad guy Curley is. He is fond of his dog even though it is old and smelly. Candy had that dog since it was a pup. His dog is his only friend; old same like Candy. Carlson offers to shoot his dog. But Candy resists for his old, smelly, nameless dog, â€Å"No, I couldn’t do that. I had `im too long.’ Candy knows if he allows his dog to be shot he will become lonelier because that is the only companion he got and he will lose it. But Carlson is determined. Eventually he leads the dog out into the darkness of the night and to its death.

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Yossarian is faking his illnesses to avoid the war. While he’s in the hospital he is required to censor letters and he will joke and censor just about anything he reads and signs off as â€Å"Washington Irving†. A Texan comes and eventually annoys everyone back into active duty. Yossarian notices that he is the only one who is concerned with the war and claims that everyone is trying to kill him. Everyone else denies that there is a war going on. He later finds out that his Colonel has raised the number of required missions from 45 to 50 when Yossarian was at 44. Orr talks to Yossarian about how he used to walk around with crab apples in his mouth. A general named Peckem hopes to take over command of Yossarian’s unit because the current general is failing at bringing enthusiasm to his subordinates. Yossarian feels sick but Daneeka instead sends him back and recommends acting like Havermeyer, who is a soldier that makes the most of every situation Yossarian talks with Daneeka whose problems are that the war is interrupting his medical practice. Yossarian interrupted educational meetings resulting in a rule that only people who don’t ask questions could ask questions. Private first class Wintergreen caused frustration among his superiors by giving a message out that only said â€Å"T.S. Eliot†. Yossarian listens to Doc Daneeka’s story about newlyweds who visited his office. Yossarian again attempts to get grounded by claiming he was crazy, which of course proved that he wasn’t. Hungry Joe has flown all of his missions and he is still not allowed to go home because the number of missions keeps rising. Orr attacks Appleby in a game of Ping-pong. Yossarians pilot, McWatt is described in this chapter as the â€Å"craziest combat man† because o... ... go through with the plan. Orr and Yossarian realize Milos amount of control as he is the mayor and even god in some countries. Nately Yossarian and Hungry Joe arrive in rome where they meet up with Nately’s prostitute. Nately argues with an old man about how America and Italy were doing in the war. Milo’s company has grown world wide but has a problem selling massive amounts of Egyptian cotton. He was surprised when Yossarian brings the idea of selling it to the government. The chaplain is miserable because no one will treat him as a regular person. He tries to help by seeing major major about the missions but he wont allow anyone in. After Colonel Cathcart periodically throws him out, he begins questioning everything even god. Nately falls in love with his whore while she is annoyed with him. Yossarian and Dunbar change identities but are caught by the Nurses

RFID Tagging :: essays research papers

RFID, which is radio frequency identification, uses tiny tags that contain a processor and an antenna and can communicate with a detecting device. RFID is intended to have many applications with supply chain and inventory control to be the drivers of utilization. RFID has been around for a long time. During World War II, RFIDs were used to identify friendly aircraft. Today, they are used in wireless systems, for example, the E-Z passes you see on the turnpikes. The major problem until recently has been cost for RFIDs. Tags have been at a cost of 50 cents, which makes it hard to utilize or really unusable for low priced items. A company based out of California called Alien Technology has invented tags for less than 10 cents a piece on large mass runs. The major benefit expected from RFID is its potential for revolutionizing the supply chain management, but RFID could have many applications, ranging from payment collections on highways, to finding lost kids in amusement parks, to prev enting cell phones from being stolen.   Ã‚  Ã‚  Ã‚  Ã‚  The RFID tag itself is about the size of a pinhead or grain of sand. The tag includes an antenna and a chip that contains an electronic product code. Industry professionals expect the RFID tag to eventually replace the barcode as identification system of choice. The electronic product code stores much more information than a regular bar code that is capable of storing information like when and where the product was made, where the components come from, and when they might perish. Unlike barcodes, which needs a line-of-sight to be read, RFIDs do not need a line-of-sight. There are two types of RFID tags call active and passive. An active tag uses its own battery power to contact the reader. It works greater distance than passive tags, but has a drawback because of the larger size. A passive tag does not require a battery, but it derives its power from the electromagnetic field created by the signal from the RFID reader. This generates enough power for the tag to re spond to the reader with its information, while the range is smaller than active tags, having no battery make the tags useful life almost unlimited and the size much smaller than active tags. In any event, the key feature of the technology is the ability for an RFID-tagged object to be tracked instantly from anywhere in the world, provided that the reader is in range.