Questions On Difficulty Sleeping At Night - 981 Words

1 2 3 4 5 A little A lot 7. I have difficulty sleeping at night. 1 2 3 4 5 A little A lot 8. I get into emotional arguments with others. 1 2 3 4 5 A little A lot 9. I do not have patience with my children, co-workers, family members, or friends. 1 2 3 4 5 A little A lot 10. My significant other makes me angry. 1 2 3 4 5 A little A lot 11. I yell and/or curse while driving. 1 2 3 4 5 A little A lot 12. I experience heart palpitations. 1 2 3 4 5 A little A lot 13. I experience tension headaches. 1 2 3 4 5 A little A lot 14. My stomach hurts. 1 2 3 4 5 A little A lot 15. My hands are shaky. 1 2 3 4 5 A little A lot Scoring: If†¦show more content†¦If they did, they would place dried beans in a sequence until one line of the card was completely filled in—either horizontally, vertically, or diagonally. The first player who called out Beano was the winner of a small Kewpie doll. After seeing how excited the crowd got at playing this game, Lowe decided to bring it back to New York with him. He bought some dried beans, a rubber numbering stamp, and some cardboard and decided to try the game out on his friends. He was thrilled to discover that they played with the same excitement as the players in Florida. During one session, Lowe noticed a particular player who was excitedly adding more beans to her card. When he called the last number, the woman jumped up, and instead of shouting, Beano, stammered out the word B-b-b-bingo! Lowe was so swept up in her excitement that he knew the game would be a smashing success. The earliest of the games came in two variations. One was a twelve-card set, which could be purchased for one dollar, and the other was a twenty-four card set sold for two dollars. You may be wondering, How is a game of bingo supposed to keep me calm? Well, I designed a special bingo board just for you, with 28 techniques designed specifically to help retrain your brain so you can gain control of your emotions and live a happier, healthier life. I think it is the perfect tool because it helps you to have a plan and options handy whenever you need them. Some days, you may struggle moreShow MoreRelatedBehavioral Interventions Designed Reduce Sleep Difficulties1722 Words   |  7 PagesAbstract The purpose of this study to investigate behavioral interventions designed to reduce sleep difficulties in a child. Positive bedtime routines and sleep restriction were successful in eliminating bedtime disturbances and nighttime awakenings in a five-year-old child sleeping problems. Positive bedtime routines included activities such as brushing teeth, taking a bath, picking out pajamas, and reading a story. Sleep restriction involved reducing the number of hours the child slept. WhileRead MoreInformative Sleep1721 Words   |  7 Pagesimportant factor for your body to restore energy. Sleeping is a time to rest your body from all the hard work you have done. It is relaxing so deeply that you lose consciousness of your surroundings. 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The Numerical Methods for Real Options - Free Essay Example

Sample details Pages: 18 Words: 5491 Downloads: 4 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? According to Mun(2006), Real Options(RO) is a systematic and comprehensive method used to value real tangible assets. The term Real Options, first used by Myers(1977), refers to the application of financial options theory to investment decisions made by firms (Krychowski and Que ´lin, 2010). RO has been of growing interest to the academic community as a promising approach to supporting investment decisions under uncertainty. Don’t waste time! Our writers will create an original "The Numerical Methods for Real Options" essay for you Create order Pioneering scholars such as Trigeorgis(1996) and Copeland(2001), have contributed valuable work to topics on real options such as the RO value in resource allocation and capital budgeting. Generally speaking, there are three main methods which are used as the tools to value the embedded RO. They are the Black-Scholes Model, the Binomial Model and Monte Carlo Simulation. Each of the method requires certain assumptions and can be best applied under specific situations. On the empirical side, RO analysis has been applied widely in a range of industries such as pharmaceutical drug development, oil and gas exploration and production, and the like. Survey results of 4,000 CFOs published in 2001 by Graham and Harvey revealed that 27% of the respondents claimed they always or almost always used some kind of options approach to evaluating and deciding upon growth opportunities (Copeland Tufano, 2004). Compared with the traditional discounted cash flow methods which assumed that the future ca sh flows can be discounted by a single fixed rate, RO analysis enjoys the merit of being highly flexible because it incorporates the managers ability to actively respond to the unfolded uncertainties. It is noted by Hall (2005) that around 30 percent of the values of high-growth, high volatility firms come from the value of embedded options. Primarily motivated by the usefulness of RO, after doing a general research on its background, I did further reading on approaches employed to value the embedded options. In this paper, my work can be divided into three parts. In the first section, a background on the real option analysis is presented. This includes an overview of typical categories of RO, the classical methods employed to value the RO and also the industrial practices of applying the RO Analysis. Additionally, a brief comparison between the traditional methods and RO is also presented. In the second section, I demonstrate the three methods in detail with elaboration on their assumptions and steps of analysis, as well as examples of application. In the final section, I conclude with a discussion of these numerical methods, including their merits and limitations as well as responses to some of the critics that RO analysis has incurred. I hope that this paper could serve as a motivator for further research. Literature Review Managers nowadays are facing a rather volatile environment because of the mixed effects of globalization, deregulation and technology break through (Krychowski and Que ´lin, 2010). RO helps them to make use the advantages of uncertainty and their flexibility. It has been crucial in the way that it helps the firm to identify, understand, value, prioritize, select, time, optimize and manage strategic investment and capital budgeting decisions (Mun, 2006). Similar to the financial option models, RO is useful both to evaluate an investment project and to determine the optimal investment timing (Krychowski and Que ´lin, 2010). The most common forms of RO, based on the division given by Copeland and Antikarov(2001) and Mun (2006), are : option to abandon, option to expand, option to switch, option to defer and sequential compound options. In detail, for example, an option to expand enables the management to expand into different markets, products, and strategies or to ex pand its current operations under the right conditions (Mun, 2006, p19). Multiple methodologies and approaches are used in RO to calculate the embedded options value. These range from using closed-form equations like the Black-Scholes model and its modifications, Binomial Models (for example, binomial lattices and binomial trees) and Monte Carlo simulations and other numerical techniques. Since this will be the main part of this paper, it will be illustrated in detail in the next section. Primarily used as a tool for strategic decision making in natural resource companies, in the recent decade, RO has been applied in a broader ranges of industries, including private equity, oil and gas exploration and production, manufacturing, IT infrastructure, pharmaceutical drug development, e-commerce and e-business, technology development, and the like ( Mun,2005). The following are some of the industry examples of applying RO. Equity According to Berger, Ofek and Swary (1996), a cons iderable proportion of equity value should be attributed to the equity holders abandonment option. They prove a difference of 11.5 percent between equity market value and the present value of cash flows for more than seven thousands firm over a 6-year period horizon. By running a series of regressions the authors find an interaction between the market value to present value premium and variables which should be linked to higher values for the abandonment option. Natural resource While evaluating the investment projects for natural resource, Brennan and Schwartz (1985) isolate the disadvantages and inadequacies of the traditional DCF approach. Particularly, they point out that obvious deficiencies are due to the overlook of the stochastic nature of output prices and of potential managerial reactions to price changes. Price uncertainty is of central concern in many natural resource industries where price fluctuates around 30 percent per year are usual. Under such circumstances the evaluation results obtained through substituting the expected values of future prices by their distributions is likely to be misleading. Oil By extending the financial option theory, Paddock, Siegel and Smith (1988) develop a new approach to valuating options on a real asset, an offshore oil lease. The authors show us how to utilize an explicit model of equilibrium in the market for the underlying real asset, i.e. the developed petroleum reserves, with option-pricing technique to derive the value of a real option. At the same time, they examine a valuation problem in sufficient detail by using the oil leases as an example. This allows close reviewing of the many theoretical and practical issues involved in applying financial option valuation theory to RO. Gold In 1998, Kelly adopts an eight period binomial option approach to estimate the value of a discovered but yet unexploited gold mine, Lihir Gold Limited. In particular, she compares the value calculated from the option mod el to what was obtained from the traditional method. The option approach appears to provide a more useful and accurate technique to evaluate the value of the gold mine . In 2002, Moel and Tufano conduct a research on the opening and closing events of 285 gold mines in North America from the year of 1988 to 1997. Strong evidence is found to support the conclusion that, compared with other methods, real (switching) options provide better explanations for the decisions on openings and closings of the gold mines. Manufacturing Newbhard, Shi and Park (2000) use a case-study to stimulate the academic and practical research needed to support a real option framework for system changes in four major manufacturing transitions which are launch of new product, commercialization of RD product, site selection of new plant and restarting production of existing commodities. By presenting a framework, they quantify manufacturing changes, develop a real option model for these activities, value the options, identify the best scenarios and integrate these elements in order to monitor and manage the overall process. They also propose a general model for optimizing real option valuation based on typical RO models such as the Black-Scholes Option Model, the Binomial Option Model. They conclude that a model that incorporates flexibility and economic factors could effectively enhance companies manufacturing strategy. Unlike the traditional valuation approaches, such as the discounted cash flow (DCF) method which bases itself on a static environment, real option analysis takes into account the potential for possible future gains and incorporates active decision making. Thus it tackles uncertainty in a better way. Specifically, deterministic models such as DCF method bases itself on some rather flawed assumptions. It assumes that all the future outcomes are fixed and can be evaluated as individual cash flows. Even more unreasonable, it seems to give a Once for All solution whic h assumes once initiated, all projects are passively managed. RO, on the contrary, accepts the facts that projects are correlated and can be actively managed through its life path. By taking the fluid environment and managerial flexibility into account, RO provides value-added insights to decision making (Mun, 2006). Numerical Methods As I have mentioned in the previous section, multiple approaches have been employed by researchers and practitioners in RO. This part will introduce the readers to three common types of methods in RO, namely, the Binomial methods, the Black-Scholes Model and Monte Carlo Simulation, from the origins of them to present application examples. More specifically, a step-by-step binomial approach is used to analyzing a compound option problem in the case study with two different techniques, so as to offer a deeper understanding for the readers. As for the Black-Scholes and Monte Carlo Simulation, due to the word limit and time constraint, I used two simplified examples from Newbhard et al (2000) and Damodaran( 2005) in the hope that the readers could have a general sense of how the two methods work. Binomial approach Work by John Cox, Steve Ross, and Mark Rubinstein has led to the creation of binomial, or lattice, models that are built around decision trees and are ideally suited to real-option valuation. As it noted by Copeland and Tufano (2004), RO dont have to be a black box. Binomial methods, with its advantage of easy math and apparent illustration has make Real option analysis a more practical tool for manager in the new era. According to Brandao et al (2005), a binomial lattice may be viewed as a probability tree with binary chance branches, with the unique feature that the outcome resulting from moving up(u) and then down (d) in value is the same as the outcome from moving down and then up. This probability tree, also referred as decision tree, can be used in modeling managerial flexibility by incorporating the decision nodes which represent decisions the managers can make to optimize the value of the project. A three-step binomial tree is illustrated below in figure1. Before enterin g details about how to use the binomial method, it is worthwhile to make certain clarifications on the assumptions behind this approach. In their book RO, Copeland and Antikarov (2001) made the marketed asset disclaimer assumption (henceforth MAD) that the market value of a project is best estimated by the present value of the project without options. Additionally, if the movements in the value of the project without options are then assumed to change over time according to a geometric Brownian motion (GBM), then the value of options can be obtained through traditional option pricing methods. Generally, there are three essential steps that need to be gone through when a binomial approach is adopted. Step1 Calculating the expected present value of the project at Time0 Step2 Obtaining estimates of the standard deviation of returns (or volatility of the project) by using a Monte Carlo simulation. Step3 Constructing a binomial tree to model the dynamics of the project value using the estimated parameters of the second step and add the decision nodes to model the projects RO. No matter what real option model is of interest, the basic structure almost always exists, taking the form: Inputs: S, X,, T, rf, b u= and d== P= Source: Mun, 2006 The basic inputs are the present value of the underlying asset(S), present value of implementation cost of the option( X), volatility of the natural logarithm of the underlying free cash flow returns in percent(,time to expiration in years(T), risk-free rate or the rate of return on a riskless asset(rf), and continuous dividend outflows in percent(b). In addition, the binomial lattice approach requires two additional sets of calculations, the up and down factors ( u and d). The up factor is simply the exponential function of the cash flow returns volatility multiplied by the square root of time-steps or stepping time (.The volatility measure is an annualized value; multiplying it by the square root o f time steps breaks it down into the time steps equivalent volatility. The down factor is simply the reciprocal of the up factor. In addition the higher the volatility measure, the higher the up and down factors. This reciprocal magnitude ensures that the lattices are recombining signs. The second required calculation is that of the risk-neutral probability, defined simply as the ratio of the exponential function of the difference between risk-free rate and dividend, multiplied by the stepping time less the down factor, to the difference between the up and down factors. In order to give the readers a more clear understanding on this, below is a case study of a sequential compound option problem, in which the execution and value of future strategic options depend on previous options. It represents a simplification of the business decision-making case and my purpose is to illustrate how a RO valuation is implemented using binomial approaches. Case Study A chemical company is considering a phased investment in a plant. There are three periods. In the beginning of year one, an initial outlay of $50 million is required to cover the cost of permits and preparation. At the end of that year, the firm has the choice to pay a commitment of $200 million to enter into the design phase. Once the design is finished one year later, the firm is believed to have a two year window during which to make the final investment in constructing the plant for $400 million. If the firm chooses not to make any investments during these two years, it can no longer to build the plant. For managers who think from the real-options perspective, this phased investment opportunity is a sequential compound option. Clearly, the initial payment of $50 million allows the firm to have the option to go on with the project for one year. At the end of year one, it again faces the choice of whether or not enter the stage of design by investing an additional $200 million . As the result, the execution of the design phase gives the firm the option to construct the plant at the end of year three or at the end of year four for $400 million. The firm estimates that if the plant existed today it would be worth $550 million by using non-option valuation techniques such as the DCF. In applying Binomial method, basically there are two techniques. The one is the decision tree approach the other is the replicating portfolio technique. I will use both of them to analyze the above case and give some comments on these two techniques. Decision Tree Analysis Prior to analyzing this problem, we must make some assumptions concerning the uncertainty in the future value of the project. We assume that current prices of the project have already incorporated all relevant information available at this point, known as part of the efficient market theory. At the same time, future changes are modeled as a random walk. This facilitates the use of a Geometric Brownian Motion (GBM) to model the dynamic uncertainty associated with prices movements (Hull, 2003). The key parameters required here are the estimated initial value, $550 million, the annual risk-free interest rate r, assumed to be 6% and the volatility, represented by, which is the annualized percentage standard deviation of the returns and is given as 18.23% here. By using the calculating structure given previously, we can calculate the corresponding values of u and d, and values for each branch of the binomial approximation. We then obtain the risk-neutral probability p ==. In this case study, the model have three periods and choose time interval to be t = 1. Therefore, u = 1.2, d =0.83, and p =0.673. For details about this binomial approximation, see Hull (2003). The value of the project is calculated via Vi,j =V0ui-jdj.For example in the right top scenario, the value of the project is $798 million which equates $550 million multiplied by 1.23. (Note: Values shown at each node in the tree are discounted Year 3 values, instead of the actual values at each point.) After approximate the project value according to the GBM, now we are going to value the Value of the Option to invest in this project. Here we used the same parameters in a decision tree with binary chance nodes to yield an equivalent binomial tree for the project value, as shown in Figure 3 below. Decision tree analysis works in the way that it models managerial flexibility in discrete time by constructing a tree with decision nodes. These nodes represent choices the manager can make to optimiz e the value of the project as uncertainties are resolved over the projects life. Note: represents a chance node in which the project can either move up or down with the probabilities of up=0.673, down=0.327 represents a decision node in which the firm can chose to invest or not denotes the termination of one possible case the line in bold shows the optimal investment strategy in different cases Lets suppose that at the end of year3, we arrive at the best scenario in which the project value is $798 million (See Figure2). If we choose to invest the extra $400 million, we will have an income of $223 million. Otherwise we will lose what we have paid for the preparation and design phase, say, $239 million. Rational managers will of course choose to invest further. The same calculation applies in the scenario with the increases in first two years and a decline in year three. By multiplying the values obtained from the decision nodes with their up and down probabilities, we arrive at the option value in year2. Using this rollback method, finally we obtain the value of option at year0, which is $31million. Replicating portfolio technique Using the binomial model which adopts replicating portfolio technique also requires two main steps. First, we need to figure out the full range of possible values for the underlying asset, in other words, draw the event tree, as shown in figure4. Figure4 (It has to be noted that, unlike the numbers for the binomial tree which have been discounted to present value, the numbers I used here are the value in that specific period.) Secondly, our task is to calculate the possible values of the project as an option at each stage. It is a backward working process and we have to begin from the end. If we abandon the project, its value is zero. Otherwise, the value at the end of that year, year three, for example, is the difference between the value of the plant at the end of year three and the expense of building it. As you can see from the figure5, we have got three potential scenarios in which the projects incremental value at the end of year three is positive and one in which th e costs of the project exceed the plants value, so the project value is zero. We now work back from the end of year three to determine the projects potential values at the end of year two. The decision rule is that in each scenario, the value will be the larger of the value of exercising the option by building the plant at that point for a cost of $550 million and the value of keeping the option window open-deferring the decision until the next period. The steps can be summarized in the followings and Figure5 serves as an illustration of the results. Step1: Calculate the potential final project values by subtracting the $400 million cost (from the event tree of Figure5). For the $314 million scenario at the bottom right of the event tree, the projects value is zero due to the cost is greater than the plant value. Step2: Obtain the potential end-of-year-two project values by comparing two calculation results. One is the value by exercising the project immediately, the oth er is the value if the project is kept alive by applying the replicating portfolio technique. Step3 ¼Ãƒâ€¦Ã‚ ¡Similar to step2, yet the number used to be compared with the value derived from replicating portfolio technique is $200 million, since immediate exercise of the project is not possible. Step4: Calculate the starting project value of $81million. Since the initial required investment is $50 million, the project is profitable. The option value is the same as what is derived by the decision tree method, which is $31 million. Figure5 Some Comments: As we can see from the above, the results obtained from Binomial Decision Tree and Replicating portfolios Techniques are largely similar. It is worthwhile to compare them briefly. The binomial approach is suggested by Copeland and Antikarov(2001), they emphasize the use of binomial lattices and replicating portfolios while Brandao et al( 2005) believe that the use of binomial trees is more intuitive appealing. The replicating approach bases itself on traditional option pricing methods, requiring that markets be complete. An important advantage of this approach to valuation is that the value of option can be calculated from market data. This avoids the estimation of the probability q of an up move in the stock price. However, for most projects involving real assets, there is no such a complete market. In this sense, using this approach appears to be complicated. Additionally, it is criticized for its computational cumbersomeness especially in a multi-stage project. Black Scholes model With their article from 1973, Fisher Black, Myron Scholes, and Robert Merton were the first to give a closed form solution for the equilibrium price for a European call option, the Black Scholes Model (BS model). It has since been the basis for numerous studies and papers about the pricing of options and empirical testing hereof. In essence, the model is a special case of the binomial model where the underlying asset is assumed to follow a continuous stochastic process instead of a discrete. Otherwise, it is based on the same underlying assumptions of no arbitrage and market replicating portfolio and that the movement of the underlying asset follows a lognormal distribution (Copeland Antikarov, 2001) It has to be noted that variants of the BS model have been made, which relaxes some of these assumptions. BS models are based on calculus of stochastic differential equation which is highly complex. So unless one can find a modified BS model that fits one own specific situation, th e process of deriving a BS model that does is very cumbersome and complex. The Black Scholes model is a so called closed form solution, meaning that a value can be found with an equation using a set of inputs. The inputs in the BS model are the same as the binomial model, with dividend as the one exception. The value of a call option( C) is calculated as: Source: Copeland Antikarov, 2001,p.106 Where and is the cumulative normal probability of unit normal variable and respectively. They are calculated as: ; Source: Copeland Antikarov, 2001, p.106 Other than the assumptions also applying to the binomial model mentioned above, the BS model has several other restrictive assumptions embedded (Copeland Antikarov, 2001 and Mun, 2002) which are: The option can only be exercised at maturity-it is a European option There is only one source of uncertainty It can only be used on a single underlying risky asset; ruling out compound options No dividends on the underlying asset The current market price and stochastic process of the underlying asset I known (observable) The variance of the underlying asset is constant over time The exercise price is known and constant over time No transaction costs Benaroch and Kauffman (1999) provide a formal theoretical grounding for the soundness of the BS option pricing model in capital budgeting methods that might be employed to assess Information Technology (IT) investments. They also demonstrate why the assumptions of both the BS and the binomial option pricing models impose constraints on the spectrum of IT investment situations that can be evaluated similarly by traditional capital budgeting methods such as discounted cash flow analysis. Most importantly, they demonstrate the first application of the BS model that uses a real-world IT business case as its experimental area. To illustrate the application of BS more clearly, I borrow an example of Brennan and Schwartz (1985) which uses option pricing theory to value a gold mine and present it with a simplified version below. Option to delay for a Gold Mine Consider a gold mine with an estimated reserve of 1 million ounces and a capacity output rate of 50,000 ounces annually. The firm maintains the ownership of this mine for the next 20 years. We expect the gold price will grow at 3% per year. It will cost $100 million to open the mine and the average variable cost is $250 per ounce; once the mine opened, the variable cost is expected to grow 5% a year. The current price of gold is $375 per ounce and its standard deviation is estimated as 20%. The riskless rate is given as 6%. The inputs to the model are summarized as follows: S = $ 47.24 million X= $100 million = 0.04 t=20 years rf= 6% Dividend Yield = = 1 / 20 = 5% Based upon these inputs, the Black-Scholes model provides the following value for the call: The value of the mine as an option is $ 3.19 million which is recognized as the mines embedded option. Monte Carlo Simulation : Because of the difficulty in obtaining the needed parameters for analytical models such as the Black-Scholes model, researchers find an alternative way to value RO by using an approximate numerical method such as Monte Carlo simulation. Monte Carlo simulation, named for the famous gambling capital of Monaco, is a very powerful methodology (Mun, 2006). Monte Carlo, in its simplest form, is a random number generator that is useful for forecasting, estimation, and risk analysis. A simulation calculates numerous scenarios of a model by repeatedly picking values form a user-predefined probability distribution, such as the normal, uniform and lognormal distributions, for the uncertain variables and using those values for the model (Mun,2006,p317-318). Boyle (1977) was among the first to propose using Monte Carlo simulation to study option valuation. What distinguishes this approach is its generality in being able to model imperfect market conditions which are difficult to be captured in other models. The Monte Carlo method proves to be most effective in situations where it is difficult to proceed using a more accurate approach (Boyle, 1977). Researchers share a common emphasis on the need for investigating practical issues related to efficiently approximating various option models via Monte Carlo simulation and including sensitivity analysis and Quasi-Monte Carlo simulation approaches (Boyle,1977; Fu and Hu,1995; Birge 1994; Newbhard, Shi and Park ,2000 ). Example: For a manufacturing company A, market research revealed a demand for a new product. The unit price of the new product is $100. The initial monthly demand for this product is 1,000 units with a standard deviation of ÃÆ' Ãƒâ€ Ã¢â‚¬â„¢ = 0.33. The product will be introduced over a four month period (T = 4) with monthly fixed interest rate of1%. Suppose we let S equal X, i.e. $100 multiplied by 1,000 which gives $100,000. To imitate the path followed by the state Variable S, we divide the phase of the variable into four time intervals. If dt is the length of one interval, then the relation between the S values is offered by Source: Newbhard, Shi and Park , 2000 Executing 1,000 Monte Carlo runs of this equation obtains an option value of $8,203, which is quite similar compared to the value of $8,155 derived from the BS model. Discussion For the practitioner, simulation opens the door for solving difficult and complex but practical problems with great ease. Monte Carlo creates artificial futures by generating thousands and even millions of sample paths of outcomes and looks at their prevalent characteristics (Mun, 2006). When modeled correctly, Monte Carlo simulation provides similar answers to the more mathematically elegant methods. Closed-form solutions can be obtained from models such as the Black-Scholes, given that a set of input parameters are estimated. BS model is exact, quick, and easy to implement with the assistance of some basic programming knowledge but is difficult to explain because applying it requires high skills of technical stochastic calculus mathematics. They are also very specific in nature, with limited modeling flexibility. Closed-form solutions are mathematically elegant but very difficult to derive and are highly specific in nature. The use of algebra distinguishes binomial models a nd enables the models to be built using standard spreadsheet software such as EXCEL. Binomial models can also be easily customized to reflect changing volatility, early decision points, as well as multiple decisions (Copeland, 2004). Another practical advantage is that because the transparency of the model, it could be understood and used by managers without very strong mathematical background. Binomial lattices, compared with close-form solutions, are easy to implement and easy to explain. They are also highly flexible but require significant computing power and lattice steps to obtain good approximation. The process of working through lattices can be cumbersome and less intuitive, which is particularly true for more complex cases to real assets with several coinstantaneous and compound options. It is important to note, however, that in the limit, results obtained through the use of binomial lattices tend to approach those derived from closed-form solutions. The choice of mod els in practice is largely depending on the underlying assets nature of changes. A possible process for deciding upon which model to use is presented below. Identify and Define RO (1) Quantify Activities Related to Changes Related to Changes (2, 3) Choose Solution Method Binomial Model Underlying change is a binomial (discrete) process Black-Scholes Model Underlying change is a lognormal (continuous) process Monte Carlo Simulation When parameter Estimation is needed REAL OPTION ANALYSIS Source: Newbhard H. B., Shi. L, Park .C (2000) Although managers today are facing a more volatile environment, most of them still rest their decisions on deterministic methods such as the discounted cash flow method, which is static in nature (Krychowski and Que ´lin, 2010). RO have problems in the implementation sector and empirical evidence shows that it is little used in practice. Whereas about 75% to 85% of firms use NPV for their investment decisions, only about 6% to 27% of them use RO analysis. Empirical studies on the implementation of RO are still rare, and research remains relatively silent on how to concretely apply RO theory (Krychowski and Que ´lin, 2010). Main Critics of options-based methods to valuing and managing growth opportunities often have two arguments. One is the huge difference between the highly complex real options and relatively simple financial options. The other is that the real option analysis relies itself on some simplified assum ptions. However, just as Copeland (2004) noted, differences do exist but are not unsolvable. Lets take the availability of information as an example. Financial options can be valued based on their underlying assets price such as the stock price. For real options, in some cases the values of their underlying assets can be observed in the same way. A coal company could estimate the value of its reserve coal by checking the expected exploitation costs and the current price of coal. It is true that at most circumstances we dont know the exact value of the underlying asset. Yet by observing comparable assets or making educated guess based on certain assumptions, we can do the estimation. In the end, option models are not the only method that requires assumptions. The main alternative to real-option analysis-discounted cash flow method-does make simplified assumptions such as the pre-committed future cash flows. Conclusion Real Options, as it is remarked by Hall(2005), is one of the most crucial important decision-making tools introduced to managers in the last three or four decades. By utilizing financial theory, economic analysis, management science and econometric modeling, real option analysis can be very useful for investment decision making in the context of uncertain environment. Managers are allowed to make flexible midcourse corrections when adopting RO (Mun, 2006). I began paper by familiarizing the readers with the essential background of RO which includes the classifications of RO and methodologies adopted to value the embedded options. The merits of RO have been discussed via a comparison with the traditional DCF method. Meanwhile, they have also been introduced to several industry applications, ranging from the equity market to manufacturing industry. Then it comes the main part of this thesis-demonstration of the three RO methods, which are the Binomial Approach, the Black and Schol es Model and Monte Carlo Simulation. A case study has been worked out via using the two techniques of the Binomial approach with the details shown on the graphs. Two simplified examples of applications of the other two methods are also included to offer the readers a general sense. Furthermore, in the discussion section, I have compared the three methods with respect to their merits and limitations. A general process of model selection is presented in a flow chart as an illustration. As for the further research, it is suggested that there is a need to find a general approach which integrates at least two models discussed in this paper. At the same time, it might also be an interesting subject to compare the usefulness of RO in different industry settings.

Snow Imagery in “Desert Places” and “Stopping by Woods on...

Robert Frost (1874- 1963). Robert Frost â€Å"was the most widely admired and highly honoured American poet of the 20th century (Eiermann).† Robert Frost was raised in rural New England where he grew a fond love for the outdoors and nature (Merriman). His love with nature elements has probably overwhelmed him so much that it has been reflected upon in many of his poems such as â€Å"The Tuft of Flowers,† â€Å"Reluctance,† and â€Å"Birches.† One of the nature imageries that have been used frequently by Robert Frost is the snow imagery. Although the snow imagery appears in many other poems by Frost we will be dealing with the poems â€Å"Desert Places† and â€Å"Stopping by Woods on a Snowy Evening.† Even though â€Å"Desert Places† and â€Å"Stopping by Woods on a Snowy†¦show more content†¦It is also because of the mysteriousness of the woods being â€Å"lovely, dark, and deep (Frost, Stopping., line 13)† that makes the speaker mesmerized. The snow in this poem gave the speaker calmness by the woods and gives the poem the mood of welcome and mysteriousness whereas in â€Å"Desert Places† the snow establishes the mood of loneliness and emptiness. Therefore, if the snow imagery made the mood of the poems different, then evidently the snow imagery will make the themes of the two poems different as well. From the different manipulations of the snow imagery on the moods in â€Å"Desert Places† and â€Å"Stopping by Woods on a Snowy Evening† it made their themes distinctly different from each other. The snow that surrounded the speaker in â€Å"Desert Places† made the field look like an open, empty, lonely place. It is from the surroundings that the speaker creates his own loneliness. From this, it is clear that the speaker’s loneliness inside himself/ herself overwhelms them so much that it causes their outlook to be of only loneliness. So in â€Å"Desert Places† the theme is that the loneliness that is created from within of the speaker causes him/her to realize or see the loneliness that is inside them and also the loneliness that surrounds them, which is nature. In â€Å"Stopping by Woods on a SnowyShow MoreRelatedRobert Frost Poetry Analysis Essay792 Words   |  4 PagesRobert Frost takes our imagination to a journey through wintertime with #8232;his two poems Desert Places and Stopping by Woods on a Snowy Evening. These two poems reflect the beautiful scenery that is present in the snow covered woods and awakens us to new feelings. Even though these poems both have winter settings they contain very different tones. One has a feeling of depressing loneliness and the other a feeling of welcome solitude. They show how the same setting can have totally differentRead MoreEssay about The Dark Side of Humanity Exposed in Robert Frosts Poetry991 Words   |  4 Pagesat the world and are often used as metaphors to describe a darker view of nature and humans. In Frosts poetry, the depth is as important as the surface. The darker aspects of Frosts poetry are often portrayed through the use of symbolism, vivid imagery, and selective word choice. Frosts poems appear to be simple on the surface, yet upon further scrutiny the poems reveal themselves as elusive. Frost utilizes ordinary objects to create a deeper meaning. For example, the poem Mending Wall, appearsRead MorePOETRY 2 11389 Words   |  46 PagesThe Inchcape Rock 7 — 11 Robert Southey 3. In the Bazaars of Hyderabad 11 — 14 Sarojini Naidu 4. Small Pain in My Chest 14 — 17 Michael Mack 5. The Professor 17 — 20 Nissim Ezekiel 6. Stopping by Woods on a Snowy Evening 20 — 23 Robert Frost 7. A Doctor’s Journal Entry for August 6, 1945 23 — 26 Vikram Seth 8. If Thou Must Love Me 26 — 29 Elizabeth Barrett Browning 9. I Believe 29 — 30 Brucellish K Sangma

Computer Programming free essay sample

Each feature of C++ exists because it has proven important for some area of industrial programming. With the language standard nearly complete, compilers that implement most of the new standard features are available now on most architecture. Real-world programmers are more interested in problems than in languages: a programming language is a way to solve a problem. When you use the right mix of languages and language features, the solution to a problem is much easier to describe and implement, with better results. C++ remains an essential tool for software engineers not because anybody thinks its the best possible language, but because its a single, portable language that works better than any alternative in each of several areas. This article explores the strengths of C++, and how to exploit them in your projects. * Why Use C++? C++ is a general purpose programming language designed to make programming more enjoyable for the serious programmer. We will write a custom essay sample on Computer Programming or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page For many uses, C++ is not the ideal language. You might prefer Tcl/Tk for writing a user interface, SQL for relational database queries, Java for network programming, or Yacc for writing a parser. C++ is used because it works well when the ideal language is (for whatever reason) not available, and because it interfaces easily with the libraries and the other languages you use. Its no accident that you can interface C++ with almost any language interpreter or library you find. You rarely find a big program written all in one language, or without using libraries, so easy integration with other languages and libraries was a key design goal. Most problems have no specialized language to solve them; some because none has (yet) been worth creating, and others because an interpreter would add too much overhead. When you cant afford a specialized language for part of a problem, a library may suffice. C++ was designed with libraries always in mind, and its most useful features are those that help you write portable, efficient, easy-to-use libraries. Reference: www. cantrip. org Why use C++ instead of Java? Java needs to be interpreted or JIT compiled, while C++ is compiled directly into machine code. At compile time the compiler with knowledge of the CPU can heavily optimize the code which is not possible in a JIT that has much stricter run time requirements. It depends on what you compare. There are shootouts that compare C++ code compiled for a 386 with 387 FPU with Suns Hot Spot engine on a current CPU. Since the java engine knows the CPU that it is running on, it can optimize for the MMX and SSE units and the more subtle issues of that specific CPU. Those shootouts are usually used to prove that java was faster than C++. If your compiler optimizes your C++ code for the CPU it will be running on, the compiler still can do more sophisticated optimizations that a Java JIT never will be able to do in the short time it has for compilation. There are also some things in the languages. Java is a more dynamic language than C++. A C++ compiler can e. g. do heavy in lining of non-virtual methods. In java all methods are what a C++ virtual method would be. Templates are completely interpreted at compile time, where generics are more or less a feature for mass casting, which needs run time support in Java. There are many more sometimes subtle differences in the languages, where well written C++ code is better for compiler optimizations than java code can ever be without losing the promises the language gives to the programmer. Of course last, not least C++ lets the programmer decide about memory management. That makes things more complicated for the programmer, but on the other hand he can decide for the memory management that is optimal for his code. A small shell until that uses a fixed block of memory every time it runs may need no memory management at all. There is no way to do that with Java, the GC is always there. Another application may be best of with pooled memo

Patriotism in Spiderman Movies Essay Example For Students

Patriotism in Spiderman Movies Essay The portrayal of the feelings of the general public is constantly used in film to stir the audience. If the filmmaker can rouse their feelings, people will be more engrossed in the movie than they would be otherwise. What is arguably the strongest emotion in many people is the feeling of patriotism. As George Bernard Shaw said, Patriotism is your conviction that this country is superior to all other countries because you were born in it. Though this feeling is not necessarily based on logic, it is none-the-less one of the most powerful feelings along with hatred and love. Depending on when a movie is made, the amount of patriotism portrayed is directly related to the political and social climate on the time. Specifically, the recent movies Spider-Man (2002) and Spider-Man 2 (2004) both show the differing amounts of patriotism that were in the American psyche at those two close but far different times in history. Additionally, it can be shown that the movies can even have their own effect on the patriotic feelings of the time. Comparing and contrasting the two movies reveals how the differing times affected the final product of both movies. Spider-Man (2002) was filmed in 2001 before and after the events of September eleventh, 2001, but wasnt released until eight months after in May of 2002. Though when the film was originally written and shot the amount of patriotism was evident, after 9/11 the political climate changed so drastically that the movie was edited to reflect this newfound patriotism in America. As the red, white, and blue Spiderman swung about the skyscrapers of New York City, no movie-goer could help but feel good about the American superhero saving the city in which only eight months earlier had been attacked so viciously. The movie shot straight into the hearts of Americans who had not experienced a climate of so much patriotism since World War II. Looking for an ultimate uniter of the people against evil, the movie-going public found him in Spiderman. Though Americans already felt great patriotism before the movies release, Spider-Man reinvigorated the public with patriotism and quite possibly caused a temporary increase in the amount of patriotism felt throughout the country. This is a definitive case where a movie not only reflects the patriotism of the era, but can also make an impact on the climate the film portrays. Spider-Man 2 (2004), though it came out soon after the first Spider-Man, used the superhero to portray patriotism very differently. The message in this movie was just as strong as the first, but the methods used to create the feeling of patriotism were not the same. By 2004 the wars in Afghanistan and Iraq had been either completed or had been going on for over a year. The American public had lost much of its blind patriotism and was very segregated when it came to the current foreign policy. One feeling that remained very passionate in almost every American was the support for our troops overseas. This dissimilar feeling of patriotism was used very well in the second Spider-Man movie, with Spiderman representing the American troops. In this movie he was still fighting for the people, protecting them from the enemy, but much more of how Spiderman was more a boy came out in this movie. In one scene when Spidermans mask has come off, a passenger seeing his face while Spiderman tries to stop the train says My God, he is just a boy. He cant be any older than my son. This, along with many other scenes in the movie use Spiderman to represent our troops overseas, many of who are teenagers themselves. This patriotic message is very strong throughout the movie, though it is less in-your-face than the first Spider-Man movie. This movie doesnt jolt the viewer into having a greater feeling of patriotism afterwards, but it does reaffirm their support for the troops and however much patriotism they felt beforehand. Though Spider-Man (2002) and Spider-Man 2 (2004) are very close together in terms of time and plotline, the way they use the superhero to bring out the movie-goers feeling of patriotism is very different. .ua29476da77bc297b608b5d50d7d2bfb1 , .ua29476da77bc297b608b5d50d7d2bfb1 .postImageUrl , .ua29476da77bc297b608b5d50d7d2bfb1 .centered-text-area { min-height: 80px; position: relative; } .ua29476da77bc297b608b5d50d7d2bfb1 , .ua29476da77bc297b608b5d50d7d2bfb1:hover , .ua29476da77bc297b608b5d50d7d2bfb1:visited , .ua29476da77bc297b608b5d50d7d2bfb1:active { border:0!important; } .ua29476da77bc297b608b5d50d7d2bfb1 .clearfix:after { content: ""; display: table; clear: both; } .ua29476da77bc297b608b5d50d7d2bfb1 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .ua29476da77bc297b608b5d50d7d2bfb1:active , .ua29476da77bc297b608b5d50d7d2bfb1:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .ua29476da77bc297b608b5d50d7d2bfb1 .centered-text-area { width: 100%; position: relative ; } .ua29476da77bc297b608b5d50d7d2bfb1 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .ua29476da77bc297b608b5d50d7d2bfb1 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .ua29476da77bc297b608b5d50d7d2bfb1 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .ua29476da77bc297b608b5d50d7d2bfb1:hover .ctaButton { background-color: #34495E!important; } .ua29476da77bc297b608b5d50d7d2bfb1 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .ua29476da77bc297b608b5d50d7d2bfb1 .ua29476da77bc297b608b5d50d7d2bfb1-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .ua29476da77bc297b608b5d50d7d2bfb1:after { content: ""; display: block; clear: both; } READ: The Atomic Bomb Flashed Above Hiroshima Essay The first is almost completely blind in .