Market strategy and recommendations to enhance the USP

Market strategy and recommendations to enhance the USP Air Asia share price has been volatile, with the possible reasons for this volatility. Marketing: Discuss the Market Strategy and recommendations to enhance the USP by using Models and diagrams. Operation Management: Analysis of prioritise operations issues which are of strategic nature for the company and possible solutions. Module Code: MGTM IM 002 Submitted to: Programme Administration Team (Kaplan Financial) Submitted by: Group 11 Group members Name: Student ID No. Chanakya C0346816 Buddha C0364220 Musharaf C0361333 Saif C0360683 Submission Date: Friday 23rd April, 2010 Table of Contents Table of Contents 2 1. Introduction: 3 2. Company Background 3 3. Meaning of Share Price Volatility: 3 3.2. Possible reasons for this volatility: 4 4.2. Mareketing Strategy of Air Asia: 8 4.3. Environmental Analysis of Air Asia on the basis of Porters Five Forces (Chart-3) 9 5.1. Air Asia Business Process 11 5.2. Air Asia prioritises issues: 12 5.3. Possible solutions 13 6. Conclusion 14 7. References: 14 1. Introduction: The company chosen for this report is Air Asia assigned by Kaplan Financial and assignment requirements as follow: A report of no more than 3500 words Possible reasons for the share volatility Market strategy to enhance USP Operational issues and possible solutions 2. Company Background Air Asia was initially established in 1996 as a full-service regional airline offering slightly cheaper fares than its competitors. It was not performing well and its turning point was 2001 when it was bought by Tony Fernandes. Tony Fernandes restructured the company and re-established in Malaysia by enrolling some low cost airline expert like Connor McCarthy (formal director of successful European LCC Ryan air) in late 2001 as first no -frills, low cost carrier(LCC) in Asia, then it got huge success and become awards winner and largest low fare airlines in the Asia. Now Air Asia flies to over 61 domestic and international destinations with 108 routes with its own 72 aircrafts and operates over 400 flights daily. Air Asia believes in the no-frills, hassle free, low fare business concept and feels that keeping costs low requires high efficiency in every part of the business. Through its unique selling point (USP), NOW EVERYONE CAN FLY, Air Asia gained a revolution in airline business with more and more people choosing as their preferred choice of transport. 3. Meaning of Share Price Volatility: Many of us use the word volatility in a loose sense, in a way that belies the accuracy with, which it can be calculated. In fact, volatility is a statistical measure of the scale of fluctuations in the price of a share, a commodity or a stock market index in the recent past. It is generally taken to be a good measure for the relative riskiness of an asset the higher the volatility, the greater the risk of losing money. Volatility, however, is not a static thing. It fluctuates sometimes quite sharply over time. (Peter Temple 02.08.2007) 3.1 Air Asia Share Price Volatility: As per Air Asia case study the share price of Air Asia has been volatile. Exhibit 5: Case Study Airline Price on 3Dec 2007 US$ Price on 11 Jan 2008 US$ Share Price Change Air Asia 0.52 0.43 -16% Period of Change in Share Price: In a very short term of a period share price of Air Asia goes down by -16% in a period of 1 month and 1 week this is extreme change in share price so this is the sign of high volatility in share price. Air Asia Volatility from 2005-2009 (Chart -1) 3.2. Possible reasons for this volatility: There are lots of reasons for share volatility here in our case study of Air Asia. We have calculated on the basis of each year that volatility graph of Air Asia shown above. Latest Information in Stock Market: Investing on share price needs lots of information about companys present situation. Investor always invests their money if they saw the potential of getting good return from that investment. So information plays vital role to attract the investors. |Market adjusts the prices up or down depending on the way the market interprets that the information will affect the companys future earnings ability. In Air Asia case study in November 2004 they listed in Malaysia Stock Exchange that air Asia is going to purchase 175-aircrft airbus A320-200 to replace Boeing 737-300s because A320-200 more fuel efficient. This information attracts the investors to invest their money because market interprets that this will affect the companys future earnings ability. We can see in chart-1 in 2005 how share price go up by +0.63% so it shows the latest information has direct impact on share price volatility. Uncertainty: Future is always uncertain some decision is made on the basis of future predictions. Investors are always keen on the past experience, current performance and future expectations of the company. Uncertainty is a major barrier for investor to make their mind to invest in share market. What exactly had happened in the Air Asia, in 2006 they choose to enter in to a domestic-route rationalising arrangement with MAS. Under this contract Air Asia took over more than two-thirds of MASs loss making routes assuming they can make profit from it as they are the LCC but this decision turns wrong because of uncertainty which is clear in chrat-1 that companys share price goes down by -5.03% . Psychological Issues on Stock Prices: Human behaviour is one of the important factor that affect the share price of the company how investor think about the company will determine whether to invest in it or not. Entering the British tycoon, founder and chairman of the Virgin Group Sir Richard Branson with 20% stake in Air Asia X in 2007. Expansion of business with Sir Richard Branson attracts the large numbers of investors in the company because psychology of people believes the successful image of Richard Branson which as a result there is hike of share price by +6.62% in the year 2007. Inflation: The overall general upward price movement of goods and services in an economy, usually as measured by the Consumer Price Index and the Producer Price Index. Over time, as the cost of goods and services increase, the value of money is going to fall because a person wont be able to purchase as much with that money as they previously could. Inflation is also one of the factors that affects the share price and leads to share volatility. As from the case study fuel cost had a significant impact on Air Asia, as it was the recession time occurs during the year 2008 were the international oil price reached at the high of about US $150 per barrel. So it was very costly for the airlines to purchase fuel at that high price which leads to increase the price of the tickets. As it was the recession time customers also could not afford the increased price plane fare so they used the alternatives way. Where there is less numbers of customers airline industries had lost Billions of dollars due to that occurrence. So inflation affected the share prices a lot. Although Air Asia tried to cover its operational cost by hedging, buying fuel-efficient airbus A320-200 and cutting fuel consumption by nearly and doubled the number of landing that we get from the tyre. They were not able to sustain on the same price of tickets. As per exhibit 6: case study. Following are the calculation: Fuel and oil ratio = fuel and oil cost à · Revenue (2008) Fuel and oil ratio= 74164.88à ·146731.5 (2008) Fuel and oil ratio= 50.5% (2007) Fuel and oil ratio= 44488.62à ·111346.3 (2007) Fuel and oil ratio= 40.0% Ά% = Current year ratio-base year ratio à · Base year ratio Ά% = 50.5-40.0 à · 40.0 Ά% = 26.25% So cost of the fuel increased by 26.25% due to hike in oil price in 2008 the cause of inflation has direct impact on companys net profit ratio if net profit is going to decrease so investor dont want to invest their money in non profit company. See chart-1 due to inflation the share price decreased by-45.34% in 2008. Demand and Supply: It is another important factor that affects the share price. Demand is willingness and ability to pay where as supply depends on the market demand and suppliers ability. When demand is high supply is less and vice versa and when demand is high price of the product is also high and is low. In case of AirAsia recession caused its demand per share down in 2008, entering of British tycoon Richard Branson increased the demand of share because of investors psychological beliefs. Restructuring of the no-frills, low cost in Asia increased the numbers of customers travelling with Air Asia which gives the US$6 million of profit December 2002 which obviously rise of demand of share which leads to increased in share price. Economic Strength of Market and Peers: Doing business in current market everyone should know the market strengthens and the competitors of the business without considering these things it is impossible to do business. The economic strength of the peers heavily affected the share price of the company. The strong competitor of the Air Asia is MAS, as it is national carrier of Malaysia. To promote healthy competition in 2006 Malaysian government brought MAS and AIRASIA together then government starts with route rationalisation of MAS where Air Asia took 96 loss making routes of MAS while these routes were operated by MAS government used to provide subsidies for the fuel cost. This is totally unfair competition which leads share price down of Air Asia in 2006 you can see in chart 1. In 2008 MAS come up with the unaccepted everyday low fare Campion .It received an over whelming response which was funded by the government it was totally win-win position for MAS and that caused negative impact on Air Asia in 2008 which is clear from chart-1. 4. Definition of Marketing Strategy: A marketing strategy is a process or model to allow a company or organization to focus limited resources on the best opportunities to increase sales and thereby achieve a sustainable competitive advantage. 4.1. Porter generic strategies: Strategy on the dimensions of strategic scope and strategic strength. Strategic scope refers to the market penetration while strategic strength refers to the firms sustainable competitive advantage. The generic strategy framework (porter 1984) comprises two alternatives each with two alternative scopes. These are Differentiation and low-cost leadership each with a dimension of Focus-broad or narrow. Product differentiation (broad) Cost leadership (broad) Market segmentation (narrow) (Chart-2) 4.2. Mareketing Strategy of Air Asia: After the restructured of the airline, the foundation is based on Low Cost and Strong Cash Flow,strategy is based on Low fare and service so goal is obviousely on high margin and sustainable growth and vision is continue to be LOWEST cost. So you can see they are totaly focusing on the Low cost Low Pricing so they getting strong cash flow sustainable growth. So they are following porters generic strategy. Their strategic scope is referring to market penetration and while strategic strength refers to the firms sustainable competitive advantage. (On the basis of chart-2) Product differentiation: Air Asia was the first airline operator in Asia to adopt with the low-fare, no-frills concept. It also becomes the regions first airline to introduce fully ticketless travel and implements free seating policy. Thats Product differentiations makes It a successful airline in the market. Cost leadership: Air Asia become the businesses that fully adopted cost leadership through operational effectiveness and efficiency. The cost advantages were enabled Air Asia to offered airfares 40% to 60% lower than those of its rivals. Some even cost less than bus fare. Even in 2008 when oil prices gone up they controlled over the cost by layered-hedge strategy to pay for fuel in advance and qualify for low price its pilots have cut fuel consumption by nearly 20%. Market Segmentation: Air Asia changes the human psychology in Asia by offering low fare and targeting middle class society. Earlier people think that only rich people can fly but then come with the No Frills, Low Cost Carrier under the tagline Now Everyone Can Fly. This whole idea captured the middle class and now no one is felling faired to fly. 4.3. Environmental Analysis of Air Asia on the basis of Porters Five Forces (Chart-3) The threat of the entry of new competitors: Air Asia is using penetration strategy under this there is always threat of the new entry exist because Air Asia is get success in the field of LCCs so now everyone want to do same business with same strategy. The intensity of competitive rivalry: Competition rivalry is currently in Air Asias favour. With price being the main battlefield of competition, Air Asia leads the way due to its low operating costs. However, there are more competitors entering the market that have major carriers as backers or owners which may lead to an unrealistic price war in the future. The threat of substitute products or services: Air Asia is domestic carrier so there is existence of substitute services are high customer can go for Bus services either for Railways or for their own transport facilities because of low distance if their services and prices are not effective. The bargaining power of customer: Today is world of Information Technology and communication everyone can find the world easily so easy availability of information and knowledge can make the bargaining power of consumer strong. E.g. customer can easily switch to other airlines by comparing the fares of different airlines. Even there are now lots competitors in LCCs so customer can easily move. The bargaining power of suppliers: For Air Asia bargaining of suppliers is major factor. In the airline industry basically there are only two major aircraft provider Boeing and Airbus. Although Air Asia is major customer of Airbus even than the bargaining power of suppliers is high due to expertise and few substitutes. 4.4. Unique Selling Proposition of Air Asia: Fernandes restructured Air Asia in 2001 , with the No Frills, Low Cost Carrier under the tagline Now Everyone Can Fly this unique selling point for Air Asia. This new business model was huge success. With this new unique selling point the company gained impressive profit. Then they come with Easy to Book, Easy to Pay Easy to Fly to encourage sale through online booking, telephone booking and through co-partner local banks and post offices. 4.5. Recommendation: Some modification in current market strategy on the bases of Marketing Mix (7Ps) to enhance Unique Selling Point. Product (service): There is a huge opportunity for Air Asia to expand its route and service and numbers of flights, they also lacking in good service in terms of flight delay. Because of it they get lots of complaints they should work on that to enhance good service Price: They are working on penetration price strategy and under this they are providing the cheapest price in the market and that is a strengths but if you see the margins per seat is very low it makes turn over high but not the net profit so they should control the operating cost to cover up that margin. So they should go with penetration. Place: Presently Air Asias putting more stress on Malaysia. Singapore, Thailand and Indonesia wherever Air Asia X only on Australia but low cost concept can also capture the market of India, China, Pakistan and Bangladesh and for long haul they can go for Europe and US. Promotions: As LCCs they are moreover targeting the middle class so to keep up the sale and to enhance USP they should come up with the new promotions scheme after a certain period of time because middle class is major customer, they should know they like changes and savings in their purchasing. People: From the starting they are only providing standard-class service on board its seems they are not targeting business class so if they come up with some of its more business concerned routs with business class they must get positive response. Process: Refers to the systems used to assist the organisation in delivering the service. Process must be very easy so that customer feels comfortable and convenient and preferred again and again. Physical Evidence: Where the service is provided. Physical Evidence is factor which makes customer to make judgement on the company or service. E.g. Customer accept what he/she spent so from the starting Air Asias tag line is No Frill airline but if you provide the frills even in the low cost it makes everyone happy. My meaning for frills is good service with the outstanding staff presentation and an unexpected interior of the craft. 5. Operation Management: The collection of people knowledge, technology, and systems within an organisation that has primary responsibility for producing and providing the organizations products or services is referred to as operations. Operations management is the planning, scheduling, control and co-ordination of the activities that transform inputs into finished goods and services. It is important because it can reduce costs, differentiate the organizations products and services and impact upon quality and therefore may increase revenue through increased customer satisfaction. (Phil Kelly 2009). This section is a process analysis to identify and prioritise current strategic operation issues and possible solution for Air Asia. 5.1. Air Asia Business Process Air Asias Operations management is focusing carefully on managing the processes to produce and distribute its services. These processes include:- Procurement:- buying various materials from suppliers and vendors Management control and coordinating functions to ensure goals are being met. Product (service):- managing the service- creation, development, distribution and sales. Quality management: important to effective operations management by continuous improvement. Inventory management:-Method like JUST-IN-TIME inventory control saves costs and improves on quicker delivery to end customers. Logistics management: focuses on the flow of services from Air Asia to its customer prioritizing on efficiency and cost effectiveness. Distribution channels. 8) Booking by mobiles is also available. 5.2. Air Asia prioritises issues: 1. Air Asia always focused on how to reduce inefficiency and make it low possible fare in the airline business: As we know they are operating penetration on market strategy to reduced costs, they even reducing the salaries and incentives of the employees, cutting down the staffs and even cutting down this facilities given to staffs and implementing the new efficient system like YMS, CRS AND ERP, which were helpful to reduced the cost but not to that extent. 2. Higher fuel cost around the world: Frequent fluctuation in fuel cost is one of the major problem for Air Asia as its emphasis on low cost their profit margin per ticket is low as price of fuel goes up they have to charge tickets price which creates negative effects in customers eyes. 3. Complain: Its because of delay in flight and even cancellation is one of the major current issues in Air Asia. 4. No maintenance, repair and overhaul: As we know they dont have their own maintenance, repair and overhaul so they have to spend on maintenance, repair and overhaul. 5. Incresing competition: At present competition in the airline industries growing day by day many full service airlines start cut costs to compete and new entry of LCC. 6. Aviation regulation and government policy: Changing rules and regulation in aviation industry and government policies is also one of the issues that Air Asia facing today. 7. Easy to buy, easy pay and easy to fly: In Malaysia Air Asia company was the first internet ticket seller airlines company. They are offering Easy to Book, Easy to Pay Easy to Fly system for their customers. So customers are easily paying and booking their ticket by online 5.3. Possible solutions 1. Cost control: As we know cost is one of the main possible solutions that can save any organisation from loss and compete in the competitive market. Air Asia can save their cost by starting their own maintenance and repair wing and overhaul routes. 2. Implementing the APS system: Implementation of APS system will provide several new functions to Air Asia. The followings are several APS functions that can help Air Asia in increasing its performance: APS system will help Air Asia in assessing suppliers performance and providing the capability to streamline monitoring process. (Aberdeen Group, 2004). Supplier portal will provide information hub for airlines and their suppliers to prevent errors happened during operational activities such as order processing (Aberdeen Group, 2004) These functions enable airlines and aircraft manufacturers and other suppliers to have collaboration strategy in managing inventory (e.g. spare parts), maintenance schedule, and design collaboration. Route profitability analysis tools enable airline companies to conduct analysis for planning efficient routes. 3. Outsourcing: Air Asia can use outsourcing strategy to be a lowest carrier in airline industry. If they can implement outsourcing strategy then they will get some benefits like, Cost benefits It will reduce risk. Outsourcing can give competitive advantage in Air Asia. Air Asia can reduce cost for IT. 4. Expansion of Air Asia business: As Air Asia is focusing on middle class people its expansion towards Indian and China has potential to gather the huge success. We know the population of Asian middle class are rising very fast. So it is a great opportunity for LCC cost airlines including Air Asia to expand their business in Asia. 6. Conclusion As we know Air Asia is a leading low fare airline in Asia. Its low cost attracts more and more customers and its market value and has increased its revenue. But knowing it from near its share price is volatile so management should focused on sustainable development with utilising marketing mix as we discussed above and should focused on customers as customers are key point for the companys success. Only low cost will not sufficient to survive in the competitive market. Providing good services like as they are saying easy to book, easy to buy and easy to fly but it is not always the facts as they are getting complains of delaying flight and even cancellation which makes bad reputation in the eyes of customers. Thats why they need to operates APS systems to overcome such a problems.

Mental Status Exam

The mental status examination or mental state examination, abbreviated MSE, is an important part of the clinical assessment process in psychiatric practice. It is a structured way of observing and describing a patient's current state of mind, under the domains of appearance, attitude, behavior, mood and affect, speech, thought process, thought content, perception, cognition, insight and judgment. [1] There are some minor variations in the subdivision of the MSE and the sequence and names of MSE domains.The purpose of the MSE is to obtain a comprehensive cross-sectional description of the patient's mental state, which, when combined with the biographical and historical information of the psychiatric history, allows the clinician to make an accurate diagnosis and formulation, which are required for coherent treatment planning. The data are collected through a combination of direct and indirect means: unstructured observation while obtaining the biographical and social information, focu sed questions about current symptoms, and formalised psychological tests.The MSE is not to be confused with the mini-mental state examination (MMSE), which is a brief neuro-psychological screening test for dementia. Theoretical foundations[edit] The MSE derives from an approach to psychiatry known as descriptive psychopathology[4] or descriptive phenomenology[5] which developed from the work of the philosopher and psychiatrist Karl Jaspers.From Jaspers' perspective it was assumed that the only way to comprehend a patient's experience is through his or her own description (through an approach of empathic and non-theoretical enquiry), as distinct from an interpretive or psychoanalytic approach which assumes the analyst might understand experiences or processes of which the patient is unaware, such as defense mechanisms or unconscious drives.In practice, the MSE is a blend of empathic descriptive phenomenology and empirical clinical observation. It has been argued that the term phenome nology has become corrupted in clinical psychiatry: current usage, as a set of supposedly objective descriptions of a psychiatric patient (a synonym for signs and symptoms), is incompatible with the original meaning which was concerned with comprehending a patient's subjective experience.ApplicationThe mental status examination is a core skill of qualified (mental) health personnel. It is a key part of the initial psychiatric assessment in an out-patient or psychiatric hospital setting. It is a systematic collection of data based on observation of the patient's behavior while the patient is in the clinician's view during the interview. The purpose is to obtain evidence of symptoms and signs of mental disorders, including danger to self and others, that are present at the time of the interview.Further, information on the patient's insight, judgment, and capacity for abstract reasoning is used to inform decisions about treatment strategy and the choice of an appropriate treatment sett ing. [9] It is carried out in the manner of an informal enquiry, using a combination of open and closed questions, supplemented by structured tests to assess cognition. [10] The MSE can also be considered part of the comprehensive physical examination performed by physicians and nurses although it may be performed in a cursory and abbreviated way in non-mental-health settings.[11] Information is usually recorded as free-form text using the standard headings,[12] but brief MSE checklists are available for use in emergency situations, for example by paramedics or emergency department staff. [13][14] The information obtained in the MSE is used, together with the biographical and social information of the psychiatric history, to generate a diagnosis, a psychiatric formulation and a treatment plan.

Optimal Hedge Ratios - Free Essay Example

Sample details Pages: 13 Words: 3757 Downloads: 6 Date added: 2017/06/26 Category Finance Essay Type Essay any type Did you like this example? Estimation of Optimal Hedge Ratios (hedging strategies): Naà ¯ve or one-to-one hedge assumes that futures and cash prices move closely together. In this traditional view of hedging, the holding of both the initial spot asset and the futures contract used to offset the risk of the spot asset are of equal magnitude but in opposite direction. In this case the hedge ratio (h) is one-to-one (or unit) (-1) over the period of the hedge. Don’t waste time! Our writers will create an original "Optimal Hedge Ratios" essay for you Create order This approach fails to recognize that the correlation between spot and futures prices is less than perfect and also fails to consider the stochastic nature of futures and spot prices and resulting time variation in hedge ratios (Miffre, City University). The beta hedge recognizes that the cash portfolio to be hedged may not match the portfolio underlying the futures contract. With the beta hedge strategy, his calculated as the negative of the beta of the cash portfolio. Thus, for example, if the cash portfolio beta is 1.5, the hedge ratio will be -1.5, since the cash portfolio is expected to move by 1.5 times the movement in the futures contract, where the cash portfolio is that which underlies the futures contract. The traditional strategy and the beta strategy yield the same value for h (Butterworth and Holmes 2001). Minimum Variance Hedge Ratio (MVHR) was proposed by Johnson (1960) and Stein (1961). This approach takes into account the imperfect correlation between spo t and futures markets and was developed by Ederington (1979). According to him, the objective of a hedge is to minimize the risk, where risk is measured by the variance of the portfolio return. The hedge ratio is identified as: h*= ?S,F / ?2F (1) Where, ?S,F is the variance of the futures contract and ?S,F is the covariance between the spot and futures position. The negative sign mean that the hedging of a long stock position requires a short position in the futures market. The relation between spot and futures can be represented as: St = ? + h*Ft + et (2) Eq. (2), which is expressed in levels, can also be written in price difference as: St – St-1 = ? + h*(Ft – Ft-1) + ?t (3) or in price returns as: St – St-1 / St-1 = ? + h*(Ft – Ft-1 / Ft-1) + ?t (4) Eq. (4) can be approximated by: logSt – logSt-1 = ? + h*(logFt – logFt-1) + ?t (5) Eq. (6) can be re-written as: RSt = ? + h*RFt + ?t (6) Where, RSt and RF t are returns on spot and futures position at time t. Equation (2) and (3) assume a linear relationship between the spot and futures while eq. (4)-(6) assumes that two prices follow a log-linear relation. Relative to equation (2)-(3), the hedge ratio represents the ratio of the number of units of futures to the number of units of spot that must be hedged, whereas, relative to eq. (4), hedge ratio is the ratio of the value of futures to the value of spot. (Scarpa and Manera, 2006) Eq. (2) can easily produce auto correlated and heteroskedastic residuals (Ederington, 1979; Myers and Thompson, 1989: cited in Scarpa and Manera, 2006). Due to this reason, some authors suggest the use of eq (3)-(6), so that the OLS classical assumption of no correlation in the error terms is not violated. Empirically, optimal hedge ratio h* can be obtained by simple Ordinary Least Square (OLS) approach, where the coefficient estimates of the futures gives the hedge ratio. This is can only be done when there is no co-integration between spot and futures prices/values and conditional variance-covariance matrix is time invariant (Casillo,XXXX). Even though application of MVHR relies on unrealistic assumptions, it provides an unambiguous benchmark against which to assess hedging performance ( Butterworth and Holmes, 2001). Error Correction Model (ECM) approach for determining optimal hedge ratio takes in to account the important role played by the theory of co-integration between futures and spot market, which is ignored by MVHR (Casillo,XXXX). The theory of co-integration is developed by Engle and Granger (1981), who shows that if two series are co-integrated, there must exist an error correction representation that permits to include both the short-run dynamics and the long-run information. ECM approach augments the standard OLS regression used in MVHR by incorporating error correction term (residual) and lagged variables to capture deviation from the long run equilibriu m relationship and short-run dynamics respectively (XXXXect). The presence of the efficient market hypothesis and the absence of arbitrage opportunity imply that spot and futures are co-integrated and an error correction representation must exist (Casillo,XXXX) of the following form: i=1 j=1 ?St = ?et-1 + Ft + ? ?i?Ft-i + ? ?j?St-j + ut (7) Where, ? is the optimal hedge ratio and et-1 = St-1 – ?Ft-1 All the above mentioned approaches employ constant variance and covariance to measure hedge ratio, which have some problems. The return series of many financial securities exhibit non-constant variance, besides having a skewed distribution. This has been demonstrated by Engle 1982, Lamoureux and Lastrapes 1990, Glosten, Jagannathan and Runkle 1993, Sentana 1995, Lee and Brorsen 1997 and Lee Chen and Rui 2001 (Rose, et al.,2005). Non-constant variance, linked to unexpected events is considered to be uncertainty or risk, and this uncertainty is particularly importa nt to investors who wish to minimize risks. In order to cope with these problems, Engle (1982) introduced the Autoregressive Conditional Heteroskedasticity (ARCH) model to estimate conditional variance. It takes into account changing variance over time, by imposing an autoregressive structure on the conditional variance. Bollerslev, Engle and Wooldridge (1988) expanded the univariate GARCH described above to a multivariate dimension to simultaneously measure the conditional variance and covariance of more than one time series. Thus, the multivariate GARCH model is applied to calculate a dynamic hedge ratio that varies over time based upon the variance-covariance between time series. (Rose, et al.,2005) Finally, other researchers have proposed more complex techniques and some special case of the above techniques for the estimation of the OHR. Among these we mention the random coefficient autoregressive offered by Bera et al. (1997), the Fractional Cointegrated Error Correction mod el by Lien and Tse (1999), the Exponentially Weighted Moving Average Estimator by Harris and Shen (2002), and the asymmetric GARCH by Brooks et al. (2002). (Casillo,XXXX) Despite the existence of massive literature on all the above approaches, no unanimous conclusion has been reached regarding the superiority of a particular methodology for determining the optimal hedge ratio. However, it would be wise to suggest that the choice of a strategy for deriving optimal hedge ratio should be based on the subjective assessment to be made in relation to investor preferences (Butterworth and Holmes, 2001). Development of Research: Figlewski (1984) conducted the first analysis of hedging effectiveness of stock index futures in US. He examined the hedging effectiveness for Standard and Poors 500 stock index futures against the underlying portfolio of five major stock indexes for the period June 1, 1982 to September 20, 1983. All five indexes represented diversified portfolio, however they were different in character from one another. Standard and Poors 500 index and New York Stock Exchange (NYSE) Composite included only the largest capitalization stocks. The American Stock Exchange composite (AMEX) and the National Association of Securities Dealers Automated Quotation System (NASDAQ) index of over-the-counter stocks contained only small companies which somewhat move independently of the Standard and Poors index. Finally, the Dow Jones portfolio contained only 30 stocks of very large firms. Return series for the analysis included dividend payments as risk associated with dividends on the portfolio is presumably one of many sources that give rise to basis risk in a hedges position. However, it was found that their inclusion did not alter the results. Consequently, and given the relatively stable and predictable nature of dividends, subsequent studies have excluded dividends. Figlewski used beta hedge and minimum variance hedge strategies and showed that the latter can be estimated by Ordinary Least Square (OLS) approach using historical data. He found that for all indexes hedge performance using minimum variance hedge ratio (MVHR) was better than beta hedge ratio was used. MVHR resulted in lower risk and higher return. When MHHR was uses, risk was reduced by 70%-80% for large capitalization portfolios. However, hedging performance was considerably reduced for smaller stocks portfolios. Also, hedging performance was better for once week and four week hedges when compared with overnight hedges. Figlewski (1885) studies hedging effectiveness of three US index futures (SP500, NYSE Composite and Value Line Composite Index (VLCI)) in hedging five US indices (SP500, NYSE Composite, AMEX Composite, NASDAQ and DJIA). Data was collected for 1982. He analyzed the hedging effectiveness for the holding period ranging from one day to three weeks using the standard deviation of the hedged position, divided by the standard deviation of the un-hedged position, as a measure of assessing hedging effectiveness. Hedge ratios were derived using beta strategy and MVHR. Assuming constant dividends, the weekly returns of each of the five indices were regressed on the on the returns of the indices underlying the three futures. Daily data was used to compute ex post risk-minimizing hedge ratios. In nearly every case, risk-minimizing hedge ratio outperformed the other in terms of hedging effectiveness, for both types of hedge ratio it was found that the hedges under a week were not very effective. It was also found that hedging was more effective for the SP500, NYSE Composite and the DJIA th an for NASDAQ and AMEX Composite. In other words, once again, portfolios of small stocks were hedged less effectively than were those comprising large stocks. Junkas and Lee (1985) used daily spot and futures closing prices for the period 1982 to 1983 for three US indices: SP500, NYSE Composite and VLCI. They investigated the effectiveness of various hedging strategies, including the MVHR and the one-to-one hedge ratio. This was done for each index using data for a month to compute the hedge ratio used during that same month in hedging the spot value of the corresponding index. MVHRs were computed by regressing changes in the spot price on changes in the futures price. The average MYHR was 0.50, whike the average effectiveness, as measured by variance of un-hedged position minus variance of hedged position divided by variance of un-hedged position (HE), was 0.72 for the SP500 and the NYSE Composite, and 0.52 for the VLCI. The effectiveness of the one-to-one hedge ratio was poor, leading to an increase in risk for the VLCI and the NYSE Composite, and an effectiveness measure of 0.23 for the SP500. In other words, MVHR was found to be most effective in reducing the risk of a cash portfolio comprising the index underlying the futures contract. There was little evidence of a relationship between contract maturity and effectiveness. Peters (1986) examined the use of SP500 futures to hedge three share portfolios; the NYSE Composite, the DJIA and the SP500 itself. MVHR and beta hedge strategy was applied to the data for the period 1984 to 1985. For each of the portfolio, MVHR gave a hedged position with a lower risk that did beta. Graham and Jennings (1987) were first to examine hedging effectiveness for cash portfolios not matching an index. They classifies US companies into nine categories according to their betas and dividend yield. For each beta-dividend yield category, ten equally weighted portfolios of ten shares each were constructed. Weekly returns w ere computed for each portfolio for 1982-83. They then investigated the performance of SP500 futures in hedging these portfolios for periods of one, two and four weeks. Three alternative hedge ratios were uses: one to one, bets and MVHR. The MVHR produced hedged positions with returns that were about 75% higher than for the other two hedge ratios. The measure of hedging effectiveness HE ranged from 0.16 to 0.33. For the one and the two week hedges, the MVHR hedge was more effective, that is, had a higher HE value. Morris (1989) investigated the performance of SP500 futures in hedging the risk of a portfolio of the largest firms in the NYSE. The data was monthly from 1982 to 1987. The MVHR was estimated using data for the entire period, and gave a HE value of 0.91. Lindhal (1992) investigated hedge duration and hedge expiration effects for the MMI and SP 500 future contract. Results showed that MVHR increased towards unity with an increase in the hedging duration. For SP 500 he dge ratios were found to be 0.927, 0.965 and 0.970 for one, two and four week hedge duration, respectively. It was concluded that hedge ratio and hedging effectiveness increase as duration increase. Lindhals examination of the hedge expiration effect is based on the fact that future prices converge towards spot prices as expiration approaches. According to him MVHR can be expected to converge towards the naà ¯ve hedge ratio if future prices also exhibit less volatility when approaching expiration. It was concluded that there was no obvious pattern in terms of risk reduction in relation to time to expiration. Unlike previous studies which only investigate ex post hedging effectiveness, Holmes (1995) became the first individual in UK to examine the hedging effectiveness of FTSE-100 stock index futures contract using Ex Ante Minimum Variance Hedge Ratio strategy. The cash portfolio being hedged mirrored FTSE-100 stock index. Data for spot and future series was collected for the per iod July 1984 to June 1992 for hedging duration of one and two weeks. The results also demonstrated the superiority on MVHR over beta hedges and showed that ex ante hedge strategy resulted in risk reduction of over 80%. Greater risk reduction was also shown to be achieved by estimating hedge ratios over longer periods. Holmes(1996) examined the ex post hedging effectiveness for the same data and return series used in the earlier study (1995) and showed that the standard OLS estimated MVHR provided the most effective hedge when compared to beta hedge strategy, error correction method and GARCH estimation. Results also suggested increase in hedging effectiveness with increase in hedging duration. This can be explained as variance of returns increases with an increase in the duration, resulting in the reduction of the proportion of the total risk accounted for by the basis risk. Butterworth and Holmes (2001) provided an unprecedented insight in to the hedging effectiveness of inv estment trust companies (ITCs) using Mid250 and FTSE100 stock index futures contract ,the former being introduced in February 1994 with an aim to provide better hedging for small capitalization stocks. Analysis is based on daily and weekly hedge durations for the cash and future return data of thirty-two ITCs and four indices for the period of February 1994 to December 1996. FTSE100 index futures and FTSE Mid250 index futures are used to hedge cash positions. Apart from well established OLS approach, consideration is also given to Least Trimmed Squares (LTS) approach for estimation which involves trimming of regression by excluding the outliers. Four hedging strategies including traditional hedge, beta hedge, minimum variance hedge and composite hedge were compared on the basis if within sample performance. Composite hedge ratio was generated by considering returns on synthetic index futures formed by weighted average of returns on FTSE100 and FTSE-Mid250 contracts. Results demonstr ated that traditional and beta hedge performed worst. MVHR strategy for daily and weekly hedges using Mid250 contracts outperformed the same strategy using FTSE100 contacts in terms of risk reduction for ITCs. However the superiority of Mid250 over FTSE100 is significantly less for cash portfolios based on broad market indexes. The composite hedge strategy demonstrated only minor improvements over results of the Mid250 contract. The LTS approach suggested similar results as OLS. Seelajaroen (2000) attempted to investigate the hedging effectiveness of All Ordinance Share Price Index (SPI) to reduce price risk of All Ordinary Index (AOI) portfolio in the Australian financial market. Hedging effectiveness was investigated for one, two and four week hedge duration. Hedge ratios were generated by using Workings model and the Minimum variance model and their effectiveness was determined by comparison with naà ¯ve strategy. Data for the analysis consisted if daily closing prices of the SPI and API for the period January 1992 to July 1998. Minimum variance model consisted of both ex post and ex ante approach. Results demonstrated superiority of both Workings model and Minimum variance model over naà ¯ve hedge strategy. Workings strategy was found to be more effective in long run, however, in short run the strategy is more sensitive to basis level used in the decision rule. Minimum variance strategy was also found to be highly effective, as even the standard use of the hedge ratio derived from past data was able to achieve risk reduction of almost 90%. Also, longer duration hedges were found to be more viable than short duration hedges and finally effects of time expiration on hedge ratio and effectiveness was found be ambiguous. DATA METHODOLOGY: This paper examines the cross hedging effectiveness of five of the worlds most actively traded Stock Index Futures to reduce the risk of KSE100 index. The 5 stock index futures include SP500, NASDAQ100, FTSE100, HANG SENG and NIKKEI 225. All 5 stock index futures and KSE100 index are arithmetic weighted indexes, where the weights are market capitalization. Analysis is based on daily and weekly hedge durations by using spot and futures return data for the period commencing from 1st January 2003 to 31st July 2008. Due to problems of sample size hedge durations of more than one week are not considered. Each daily return series consists of 1457 observations, out of which last 157 (from 1st January 2008 to 31st July 2008) are used to calculate out of sample (ex ante) hedging performance. Each weekly series consists of 292 observations, out of which last 31 (from 1st January 2008 to 31st July 2008) are used to measure ex ante hedging performance. The return series for each index is calcu lated as a logarithmic value change: Rt = logVt – logVt-1 (2) Where, Rt is the daily or weekly return on either the spot or futures position and Vt is the value of the index at time t. Value is the daily or weekly closing value of all 6 indexes. All data was obtained from Datastream. Two hedging strategies are considered. First, is the MVHR, and the second, is an extension of the first strategy by applying the theory of co-integration, formally known as Error Correction Model. MVHR is estimated by regressing spot returns (KSE 100 in this case) on futures returns using historical information: RSt = ? + bRFt + et (3) Where, RSt is the return on KSE100 index in time period t; RFt is the return on the futures contract in the time period t; et is the error term and ? and b are regression parameters. Value of b is obtained after running the above regression in e-views, which is the hedge ratio h* shown earlier in equation 1. This hedge ratio is used in furt her calculation for determining risk reduction. Effectiveness of minimum variance hedge is determined by examining the percentage of risk reduced by the hedge (Ederington, 1979; Yang, 2001). Consequently, hedging effectiveness is measured by the ratio of the variance of the un-hedged position minus the variance of the hedged position, divided by the variance of the un-hedged position (Floros, Vougas 2006). Var(u) = ?2s (4) Var(h) = ?2s + h2?2F – 2h?S,F (5) Hedging Effectiveness (HE) = (Var(u) Var(h)) / Var(u) (6) Where, Var(u) is the variance on un-hedged position (KSE100); Var(h) is the variance on the hedged position; ?S ?F are standard deviation on spot (KSE100) and futures returns respectively; h is the value of hedge ratio (b in equation 3); and ?S,F is the covariance between spot and future returns. Error Correction Model (ECM) approach requires testing for co-integration. The return series are checked for co-integration by following a simple two step a pproach suggested by Engle and Granger. Consider two time series Xt and Yt, both of which are integrated of order one (i.e. I(1)). Usually, any linear combination of Xt and Yt will be I(1). However, if there exists a linear combination (Yt – ?Xt) which is I(0), then according to Engle and Granger, Xt and Yt are co-integrated, with the co-integrating parameter ?. Generally, if Xt is I(d) and Yt is I(d) but their linear combination (Yt – ?Xt) is I(d-b), where b0 then Xt and Yt are said to be co-integrated. Co-integration conjoins the long-run relationship between integrated financial variables to a statistical model of those variables (XYZ,200N). In order to test for co-integration, it is essential to check that each series is I(1). Therefore, the first step, is to determine the order of integration of each series. Order of integration is determined by testing for unit root by using Augmented Dickey Fuller (ADF) test. A variable Xt is I(1), if it requires differenc ing once to make it stationary. The null of unit root is rejected when probability is less than the critical level of 5%. Then the following OLS regression is estimated: RSt = ? + bRFt + et Where, variables are same as equation 3. Empirical existence of co-integration is tested by constructing test statistics from the residuals of the above equation. If two series are co-integrated then et will be I(0). This is found by testing the residuals for unit root by using ADF test. The null of unit root is rejected if probability is less than 5%. Once it is established that the series are co-integrated, their dynamic structure can be exploited for further investigation in step two. Engle and Granger show that co-integration implies and is implied by the existence of an error correction representation of the series involved. Error correction model (ECM) abstracts the short- and long-run information in modeling the data(XYZ,200N). The relevant ECM to be estimated for generation of the optimal hedge ratio is given by: j=1 i=1 RSt = ?et-1 + ?RFt + ? ?iRFt-i + ? ?jRSt-j + ut (7) Where, et-1 is the error correction term and n and m are large enough to make ut white noise; ? is the hedge ratio. The appropriate values of n and m are chosen by the Akaike information criterion (AIC) (Akaike1974). In short, returns on KSE100 are regressed on futures returns and residuals are collected by using OLS. ECM with appropriate lags is estimated by the OLS in the second stage. Next phase is to determine the superiority of the two models MVHR and ECM, which were used to obtain the hedge ratios b and ? respectively. This is achieved by conducting Wald Test of Coefficient on model (7). If anyone of the lags in model 7 turn out to be significant, then optimal hedge ratio obtained through model (7) will be superior then hedge ratio obtained through model (3). Hence, signaling the superiority of ECM over MVHR. The significance is tested by a hypothesis, where: Ho= C(1)=C(2)†¦=C(i)=0 H1 = C(1)=C(2)†¦=C(i)?0 The null is rejected if the probability of Chi-square statistic is less than the critical value of 5%. Lastly, the superior hedge ratio will be used to determine ex ante performance. The hedging effectiveness of the superior hedge ratio will be based on the measure of risk reduction achieved through equation (6).

The Love Canal Crisis An Epidemic - 1709 Words

James Daniel Mr. Peters Honors Environmental Science 11 November 2016 A Civil Action The Love Canal crisis is an epidemic that will shake the roots of this country for centuries. From 1942 to 1953 a landfill in Niagara Falls area known as the Love Canal; which is named after 18th-century famous entrepreneur William T. Love who had an aspiration to connect two levels of the Niagara River, which evidentially separated by Niagara Falls. His plan sought to integrate a canal that would utilize hydroelectric power to create energy. His plan was an utter failure due to the economic collapse of 1892. The construction of the canal continued for a while prior to the economic collapse. After only one mile, at fifteen feet wide and ten feet deep, the canal had been partly dugged. The canal was sold in 1920 to the city of Niagara falls which began using it as an area for chemical waste disposal, followed by the United States Army burying waste from chemical warfare experiments. Hooker Chemical and Plastics Corporation attained the property in 1947 and buried 21,000 tons of toxic waste for the next five years. During this duration, the Niagara population was extending rapidly, and the city was highly desperate for land. The city purchased the land for $1. A school was constructed on the landfill which punctured a copper barrier Hooker had used to enclose the chemical waste. Later on, Health reports and very odd, strange odors were reported the following years, but not until theShow MoreRelatedThe Prevention of Water Contamination: Mission Impossible?1503 Words   |  7 Pagesinstead, pollute it. The right to water is not officially a human right. However, because â€Å"water is a basic need for human development, health, and well-being†¦ it is an internationally accepted human right† (Thompson 3). Water contamination is an epidemic that is so common these days that it is no longer shocking to even hear that water is contaminated. Water contamination negatively effects water that is used on a daily basis. Continuing on in this way will eventually lead to contamination that willRead MoreDbq for American Imperialism3893 Words   |  16 Pagesto the United States in perpetuity, the use, occupation and control of a zone of land and land under water for the construction, maintenance, operation, sanitation and protection of said Canal of the width of ten miles extending to the distance of five miles on each side of the center line of the route of the Canal to be constructed; the said zone beginning in the Caribbean Sea three marine miles from mean low water mark and extending to and across the Isthmus of Panama into the Pacific Ocean to aRead MoreMigration in the 19th Century5601 Words   |  23 Pagesthroughout the country. Although the country was afflicted by several diseases like the plague in 1813 and cholera this did little to curb the unstoppable growth in population. Many inhabitants saw emigration, as the only possible solution to avoid the crisis of overpopulation, which would have left them with no or little income. Especially men left their home country for a better occupation, whilst women stayed at home or followed later (Dr. C. Cassar). In the early years, emigration was primarily anRead MoreCase Study in Nursing8060 Words   |  33 Pages hypotension, lack of resistance to cold, and inability to respond physiologically to  stress. Even patients who produce adequate corticosteroids under normal conditions usually cannot meet the increased requirement. Life-threatening Addisonian crisis can then occur. A deficiency of aldosterone can lead to sodium depletion, dehydration, and circulatory collapse. In congenital lipoid hyperplasia (adrenal cortex with male pseudo-hermaphroditism [false presence of both ovarian and testicular tissue])Read MoreCase Studies67624 Words   |  271 PagesPreparing an effective case analysis C-3 CASE 1 CASE 2 CASE 3 CASE 4 CASE 5 CASE 6 CASE 7 ABB in China, 1998 C-16 Ansett Airlines and Air New Zealand: A flight to oblivion? C-31 BP–Mobil and the restructuring of the oil refining industry C-44 Compaq in crisis C-67 Gillette and the men’s wet-shaving market C-76 Incat Tasmania’s race for international success: Blue Riband strategies C-95 Kiwi Travel International Airlines Ltd C-105 CASE 8 Beefing up the beefless Mac: McDonald’s expansion strategies