Intelligent Public Transport System Design

Intelligent Public Transport System Design An Intelligent Public Transport System for Smart City Gurnoor Walia, Kuljit Kaur Abstract Road safety has changed into a main subject for governments and automobile manufacturers in the last decade. The advancement vehicular technologies has privileged researchers, institutions and companies to target their efforts on improving road safety. new kinds of networks, such as for instance Vehicular Ad Hoc Networks (VANETs), have now been designed to assist communication between vehicles themselves and between vehicles and infrastructure. Smart cities embrace intelligent traffic management in which data from the Traffic Information Centre (TIC) infrastructures might be accessible at any point. In this paper we have listed the details of various features relating to intelligent transportation system. INTRODUCTION Cities are complex, networked and continuously changing social ecosystems, shaped and transformed through the interaction of different interests and ambitions. Cities represent a promise for future years: a vision of creativity, opportunity, freedom and prosperity. More than half of the global population has become urban and surveys estimate this percentage may even grow towards 70% in 2050 [2]. The services are increasingly enabled by broadband infrastructures, Internet-based networked applications, wireless sensor networks, open data and open platforms. Within the last decade digital technologies have begun to cover our cities, working together to make the backbone of a big, intelligent infrastructure. Wireless telecommunications grids and broadband fiber-optic are supporting smart phones, mobile phones and tablets which can be increasingly affordable. Add to this foundation a uncompromisingly growing network of sensors and digital control technologies such as smart meters, all tie d together by inexpensive, powerful computers and our cities are quickly fitting like computers in open air[3]. Smart City A smart city use digital technologies to boost the performance and wellbeing, to decrease costs and resource consumption, and also to engage more successfully and actively with its citizens. The core smart sectors comprise energy, health care, transport, water and waste. It should be able it to respond more rapidly to needs of a city and global challenges than one with a simple transactional association with its people. Interest in smart cities is motivated by major challenges, including economic restructuring, climate change, ageing populations, the move to online retail and entertainment, and pressures on public finances.[4] The terms ‘intelligent city’ and ‘digital city’ are also used. [5][6].According to IEEE A smart city brings together technology, government and society to enable the following characteristics: smart cities, a smart economy, smart mobility, a smart environment, smart people, smart living, and smart governance.[7]. 1.3 Intelligent Transportation System As India plans to take a leap forward with approval for smart cities, intelligent transportation is a must have technology. Intelligent transportation systems (ITS) are applications which, without embodying intelligence as such, intend to offer innovative services relating to traffic management and different modes of transport and enable users to be much better informed and make safer, more synchronized, and smarter use of transport networks. Intelligent transport systems differ in technologies used, from basic management systems such as traffic signal control systems; car navigation; container management systems; automatic number plate recognition; variable message signs or speed cameras to observe such applications, such as security CCTV systems; and to more complex applications that combine live data and feedback from numerous sources, such as weather information; parking guidance and information systems; bridge de-icing (US deicing) systems; etc. INTELLIGENT TRAFFIC SYSTEM USING VANETs The development of new vehicular technologies has shifted companies, researchers and institutions to focus their efforts on improving road safety. The evolution in wireless technologies has allowed researchers to style communication systems where vehicles directly take part in the network. Thus networks such as for instance VANETs are produced to facilitate communication between vehicles themselves and between vehicles and road side unit (infrastructure). Vehicular ad hoc network (VANET) is a technology which uses moving cars as nodes in a network to make a mobile network [10]. VANETs are becoming a useful consideration due to the various important applications related to traffic controlling road safety. Smart cities saturated in traffic want to minimize their transportation problems due to the increasing population that results in congested roads. VANET helps to fix this issue by improving vehicles mobility and also helps at having more secured and sophisticated cities. VANETs provide easier communication facility among vehicles and also with fixed infrastructure. This can not merely improve the trail safety, but also gives benefits commercially. Pollution reduction, accidents prevention, congestion reduction and safer roads are some of the benefits of VANETs. The development of an efficient system in VANETs has many important benefits, to the traffic police as well as to the drivers. Proper traffic alerts and updated information about traffic incidents will make safe driving, increase road safety and reduce the traffic jams in the city. It also helps to indentify where the traffic rules are violated. Furthermore, it also helps economically; real-time traffic alerting will reduce trip time and fuel consumption and therefore decrease pollution as well [11]. So it is definitely beneficial in many ways. TECHNIQUES FOR IMPELMENTING VARIOUS ASPECTS OF VANETS The smart city can utilize VANETs by having intelligent traffic lights (ITLs) set in the crossroads of a city. These ITLs gathering traffic information (e.g. traffic density) from the passing vehicles, updating traffic statistics (congestion) of the city and reporting those statistics to the vehicles to ensure that vehicle can select the very best path that is congestion free. Also, ITLs will send warning messages to vehicles in case accident occurs to prevent further collisions. As [14], the proposal manages traffic information to be able to avoid accidents, though the information here is gathered from the vehicles themselves so no more infrastructure is needed. Also the system could easily be utilized by the traffic information centre to style an adaptive traffic light system similar to [12] and [13]. The proposed system architecture [16] is as shown in figure 4. Figure 4. The proposed System architecture [16] with intelligent traffic lights It is assumed that vehicles have a global positioning system (GPS), aboard unit, full map information of the city including the exact position of the each ITL, to ensure that vehicles can very quickly select the nearest ITL. Warning message is of three types: yellow circle indicates that vehicle is independent and not communicating with every other vehicle, green circle indicates communication is made and messages transition is certainly going on red and signal indicates two vehicles come closer and there could be the chances of collision as shown in figure 4. Inter-vehicular communication is presented based on an adaptive traffic signal control system [12]. This system reduces the waiting time of the vehicles at the square also results in decrease in waiting time at the signal. To realize this system, the concept of clustering is used to collect the data of the vehicles coming towards the intersection. System that takes the control decisions based on the information coming from the vehicles is very well described by the authors [13]. Every vehicle is equipped with a short range communication device and controller nodes are placed in the intersection with traffic lights. This controller node at intersection acts as adaptive control signal system. In [12] and [13] two adaptive traffic light systems based on wireless communication between vehicles and fixed controller nodes deployed at squares are designed. Both systems improve traffic fluency, reduce the waiting time of vehicles at squares and help to avoid collisions. The work in [14] is a survey about multifunctional data driven intelligent transportation system, which collects a large amount of data from various resources: Vision-Driven ITS (input data collected from video sensors and used recognition including vehicle and pedestrian detection); Multisource-Driven ITS (e.g. inductive-loop detectors, laser radar and GPS); Learning-Driven ITS (effective prediction of the occurrence of accidents to enhance the safety of pedestrians by reducing the impact of vehicle collision);and Visualization-Driven ITS (to help decision makers quickly identify abnormal traffic patterns and accordingly take necessary measures). But, it requires large amount of memory to stores the videos. The e-NOTIFY [15] system was designed for automatic accident detection, which sends the message to the Emergencies Center and assistance of road accidents using the capabilities offered by vehicular communication technologies. The e-NOTIFY system combines both V2V and V2I communications to efficiently notify an accident situation to the Control Center. A technique of finding water-logging-prone areas is given in [8]. This recognition technique is principally based on the following steps. (i) Prediction of locations of low valleys in a sound prone 2D curve. (ii) Confidence score obtained from the calculation of valley area. The proposed solution could easily be integrated with participatory sensing for smart cities. If the smart-phone users voluntarily submit the GPS information received in their hand-held devices, the same can be used for water logging zone calculation. This can help the city authority to create a dynamic water logging prone map of the entire city. In [9] researchers propose a radically different road pricing scheme to avoid and decrease the traffic congestion in metropolises. Unlike designating a small congestion charge zone in an area, they propose to employ a road pricing system over the entire city. Thus, the road pricing system can control the traffic flow in the whole traffic network of the city. Furthermore, the road costs are adjusted dynamically on the basis of the instantaneous traffic densities of every road in the city in order torapidly and efficiently control the traffic flow and to prevent the traffic congestion. Geographical source routing is just a promising routing technique for VANETs, because adaptability for network dynamics and ability to take care of topology holes. In traditional geographical source routing algorithms a best-known neighbor, usually the neighbor nearest to another junction in a greedy fashion, is designated as the following hop. This method may cause two drawbacks: (1) the designated neighbor mightnt have the packet correctly and (2) non-neighbor nodes are never given opportunities to complete forwarding. In [1],two problems are solved by introducing the thought of opportunistic routing to geographical source routing. A new routing protocol, named Geographical Opportunistic Source Routing (GOSR), is developed. GOSR allows non-neighbor nodes as well as the best-known neighbor to become forwarder. The notification cost of opportunistic routing is minimized by enforcing a scope from which candidate forwarders are selected. Defer timers are adopted in order to avoid confl icts due to simultaneous transmissions by nodes in the designated scope. Simulation results also reveal that GOSR can substantially reduce hop count and also improve end-to-end delivery ratio remarkably. TOOLS USED FOR SIMULATING VANETS It is significant to estimate the performance of any network in order to highlight any issues that may exist; the most appropriate way to accomplish this task is therefore to deploy simulations that provide the closest results to real-world annotations. Various simulation tools have been used to evaluate and simulate the performance of routing protocols in VANET. 5.1 Network simulator (NS2 and NS3 ) The NS-2 provides significant support for the simulation of TCP, routing and multicast protocols over wired and wireless networks. The NS-2 simulator is written in C++ with an OTcl (Object Tool Command Language) interpreter as a command and configuration interface. C++ is fast to run but slower to change, making it appropriate for use in comprehensive protocol implementation. NS3 is exclusively written in C++ and it is available for different platform such as Windows, Linux, Unix and OSX, with the coding limited to only a few hundred lines as opposed to 300,000 lines for NS-2. For the sake of huge network simulation,NS3 has come to support distributed and federated simulation tasks. NS-3 is free software available for researchers and developers in order to simulate internet protocols and huge systems in a controlled environment. 5.2 GlomoSim GlomoSim was developed to simulate wireless network simulation. It was coded in Parsec, in which all new protocols need to be described. GlomoSim has the ability to run on SMP (shared-memory symmetric processor: memory simultaneously accessible by all programs) and to assist in dividing the network into separate modules, each running as a distinct process. This decreases the load on the CPU by dividing its workload. GlomoSim supports multiple wireless technologies. GlomoSim was developed to support million of nodes as a single simulation. 5.3 MOVE The mobility model generator for vehicular networks is based on the Java programming language and is built on SUMO (Simulation of urban mobility). MOVE has greater consideration of traffic levels supported by GUI facilities. Mobility trace files can be generated from the Google Earth or TIGER databases. Custom (random and user) graphs a real so supported, although the node movement is constrained to a grid in a random graph. 5.4 TraNs TraNs (traffic and network simulator environment) is based on Java with a visualization tool to integrate SUMO and NS-2 and is specially designed for VANET (Traffic and network simulation environment) in a single module to support vehicular simulation. This can be accomplished by converting traffic files in to a dump file by SUMO. This file can then be read by NS-2. 5.5 VANET MobiSim VANET MobiSim was developed to overcome the limitations of CanuMobiSim. It supports car-to-car and car-to- infrastructure communications, which support stop signs, traffic lights and activities based macro-mobility with the support of human mobility dynamics. TIGER, GDF and random and custom topology are used to obtain road and traffic topology. Vanet MobiSim uses a parser to obtain the topology from GDF or TIGER. 5.6 NCTUns NCTUns (National Chiao Tung University Network Simulator) (WangandLin,2008) is built using C++ programming language with a high level of GUI support. The user has less need to be concerned about code complexity. NCTUns combines the traffic and network simulators in a single module, making a distinct vehicular network environment available. NCTUns supports the ITS (intelligent transport system) environment by using automatic road assignment supported by the SHARPE-format map file. Vehicle movement can be controlled automatically. FUTURE WORK and CONCLUSION In previous work researchers have designed a smart city framework for VANETs including intelligent traffic lights (ITLs) that transmit warning messages and traffic statistics. Simulation results reveal that the usage of ITLs in smart cities can not merely improve road safety but also the drivers quality of life. They have explained the way the ITLs gather traffic and weather conditions of the roads and how they update those statistics. The goal is that the drivers assistant device usually takes proper trip decisions, for instance in order to avoid congested roads, and therefore reducing the trip time and pollution as well. As a near future work, ITLs could communicate to passing vehicles indicating where would be the free parking spots in the city. With this specific information, the driver assistant device could indicate the driver where free spots are located. This technique could use a WSN to obtain the data about free parking spots and communicate it to the nearest ITLs. The ITLs could share that information although sub-network they form. This might save trip time, petrol and CO2 as a consequence, which helps to own sustainable smart cities. Also, statistics collected by the ITLs can improve data routing protocols selecting the road that offers an increased chance to forward a supply to the destination successfully. A VANET routing protocol that considers those statistics in its operation can also be designed. REFERENCES [1] Zhongyi, L., Tong, Z., Wei, Y., and Xiaoming, L., â€Å"Poster Abstract: GOSR: Geographical Opportunistic Source Routing for VANETs,† Mobile Computing and Communications Review, Vol. 13, No. 1, January 2009 [2] United Nations, â€Å"World Urbanization Prospects, The 2007 Revision Highlights,† United Nations, New York, 2008. [3] Schaffers, H., Ratti, C., and Komninos, N., â€Å"Special Issue on Smart Applications for Smart Cities – New Approaches to Innovation,† Journal of Theoretical and Applied Electronic Commerce Research, Universidad de Talca – Chile, Dec 2012 [4] Dept Business, Challenges Faced by Cities and the Need for Smarter Approaches, pg-5, 2013 [5] Moir, Challenges Faced by Cities and the Need for Smarter Approaches, pg-18, 2014 [6] Smart City, http://en.wikipedia.org/wiki/Smart_city [7] IEEE Smart Cities ,http://smartcities.ieee.org/about.html [8] Choudhury, A.D., Agrawal, A., Sinha, P., Bhaumik, C., Ghose, A., and Bilal, S., â€Å"A Methodology for GPS-based Water logging Prediction and Smart Route Generation,† 12th International Conference on Intelligent Systems Design and Applications (ISDA), Kochi , 2012. [9] Soylemezgiller, F., Kuscu, M., and Kilinc, D., â€Å"A Traffic Congestion Avoidance Algorithm with Dynamic Road Pricing for Smart Cities,† presented at IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks, London, 2013 [10] Emmelmann, M., Bochow, B., and Kellum, C.C., â€Å"Vehicular networking: Automotive applications and beyond,† John Wiley and Sons, 2010. [11] Ferrari, G., Busanelli, S., Lotti, N., and Kaplan, Y., â€Å"Cross- Network Information Dissemination in VANETs,† 11th International Conference on ITS Telecommunications, pp. 351-356, 2011. [12] Maslekar, N., Boussedjra, M., Mouzna, J., and Labiod, H., â€Å"VANET based Adaptive Traffic Signal Control,† IEEE 73rd Vehicular Technology Conference (VTC Spring), pp. 1-5, 2011. [13] Gradinescu, V., Gorgorin, C., Diaconescu, R., Cristea, V., and Iftode, L., â€Å"Adaptive Traffic Light Using Car-to-Car communications,† IEEE 65th Vehicular Technology Conference (VTC Spring), pp. 21-25, 2007. [14] Junping, Z., Fei-Yue, W., Kunfeng, W., Wei-Hua, L., Xin, X., and Cheng, C., â€Å"Data-Driven Intelligent Transportation Systems: Survey,† IEEE Transactions on Intelligent Transportation Systems, Vol. 12, Issue 4, pp. 1624-1639, 2011. [15] Fogue, M., Garrido, P., Martinez, F. J., Cano, J. C., Calafate, C. T., Manzoni, P., and Sanchez, M., â€Å"Prototyping an Automatic Notification Scheme for Traffic Accidents in Vehicular Networks,† Wireless Days (WD) IFIP, pp. 1-5, 2011. [16] Khekare, G.S., Sakhare, A.K., â€Å"Intelligent Traffic System for VANET: A Survey,† International Journal of Advanced Computer Research (2277–7970) Volume-2 Number-4 Issue 6, December 2012.

Anomalies in Option Pricing

Anomalies in option pricing: the Black-Scholes model revisited New England Economic Review, March-April, 1996 by Peter Fortune This study is the third in a series of Federal Reserve Bank of Boston studies contributing to a broader understanding of derivative securities. The first (Fortune 1995) presented the rudiments of option pricing theory and addressed the equivalence between exchange-traded options and portfolios of underlying securities, making the point that plain vanilla options – and many other derivative securities – are really repackages of old instruments, not novel in themselves. That paper used the concept of portfolio insurance as an example of this equivalence. The second (Minehan and Simons 1995) summarized the presentations at â€Å"Managing Risk in the '90s: What Should You Be Asking about Derivatives? â€Å", an educational forum sponsored by the Boston Fed. Related Results Trust, E-innovation and Leadership in Change Foreign Banks in United States Since World War II: A Useful Fringe Building Your Brand With Brand Line Extensions The Impact of the Structure of Debt on Target Gains Project Management Standard Program. The present paper addresses the question of how well the best-known option pricing model – the Black-Scholes model – works. A full evaluation of the many option pricing models developed since their seminal paper in 1973 is beyond the scope of this paper. Rather, the goal is to acquaint a general audience with the key characteristics of a model that is still widely used, and to indicate the opportunities for improvement which might emerge from current research and which are undoubtedly the basis for the considerable current research on derivative securities. The hope is that this study will be useful to students of financial markets as well as to financial market practitioners, and that it will stimulate them to look into the more recent literature on the subject. The paper is organized as follows. The next section briefly reviews the key features of the Black-Scholes model, identifying some of its most prominent assumptions and laying a foundation for the remainder of the paper. The second section employs recent data on almost one-half million options transactions to evaluate the Black-Scholes model. The third section discusses some of the reasons why the Black-Scholes odel falls short and assesses some recent research designed to improve our ability to explain option prices. The paper ends with a brief summary. Those readers unfamiliar with the basics of stock options might refer to Fortune (1995). Box 1 reviews briefly the fundamental language of options and explains the notation used in the paper. I. The Black-Scholes Model In 1973, Myron Scholes and the late Fischer Black published their seminal paper on option pricing (Black and Scholes 1973). The Black-Scholes model revolutionized financial economics in several ways. First, it contributed to our understanding of a wide range of contracts with option-like features. For example, the call feature in corporate and municipal bonds is clearly an option, as is the refinancing privilege in mortgages. Second, it allowed us to revise our understanding of traditional financial instruments. For example, because shareholders can turn the company over to creditors if it has negative net worth, corporate debt can be viewed as a put option bought by the shareholders from creditors. The Black-Scholes model explains the prices on European options, which cannot be exercised before the expiration date. Box 2 summarizes the Black-Scholes model for pricing a European call option on which dividends are paid continuously at a constant rate. A crucial feature of the model is that the call option is equivalent to a portfolio constructed from the underlying stock and bonds. The â€Å"option-replicating portfolio† consists of a fractional share of the stock combined with borrowing a specific amount at the riskless rate of interest. This equivalence, developed more fully in Fortune (1995), creates price relationships which are maintained by the arbitrage of informed traders. The Black-Scholes option pricing model is derived by identifying an option-replicating portfolio, then equating the option's premium with the value of that portfolio. An essential assumption of this pricing model is that investors arbitrage away any profits created by gaps in asset pricing. For example, if the call is trading â€Å"rich,† investors will write calls and buy the replicating portfolio, thereby forcing the prices back into line. If the option is trading low, traders will buy the option and short the option-replicating portfolio (that is, sell stocks and buy bonds in the correct proportions). By doing so, traders take advantage of riskless opportunities to make profits, and in so doing they force option, stock, and bond prices to conform to an equilibrium relationship. Arbitrage allows European puts to be priced using put-call parity. Consider purchasing one call that expires at time T and lending the present value of the strike price at the riskless rate of interest. The cost is [C. sub. t] + X[e. sup. -r(T-t)]. (See Box 1 for notation: C is the call premium, X is the call's strike price, r is the riskless interest rate, T is the call's expiration date, and t is the current date. At the option's expiration the position is worth the highest of the stock price ([S. sub. T]) or the strike price, a value denoted as max([S. sub. T], X). Now consider another investment, purchasing one put with the same strike price as the call, plus buying the fraction [e. sup. -q(T-t)] of one share of the stock. Denoting the put premium by P and the stock price by S, then the cost of this is [P. sub. t] + [e. sup. -q(T-t)][S. sub. t], and, at time T, the value at this position is also max([S. sub. T], X). (1) Because both positions have the same terminal value, arbitrage will force them to have the same initial value. Suppose that [C. sub. t] + X[e. sup. -r(T-t)] [greater than] [P. sub. t] + [e. sup. -q(T-t)][S. sub. t], for example. In this case, the cost of the first position exceeds the cost of the second, but both must be worth the same at the option's expiration. The first position is overpriced relative to the second, and shrewd investors will go short the first and long the second; that is, they will write calls and sell bonds (borrow), while simultaneously buying both puts and the underlying stock. The result will be that, in equilibrium, equality will prevail and [C. sub. t] + X[e. sup. r(T-t)] = [P. sub. t] + [e. sup. -q(T-t)][S. sub. t]. Thus, arbitrage will force a parity between premiums of put and call options. Using this put-call parity, it can be shown that the premium for a European put option paying a continuous dividend at q percent of the stock price is: [P. sub. t] = -[e. sup. -q(T-t)][S. sub. t]N(-[d. sub. 1]) + X[e. sup. -r(T-t)]N(-[d. sub. 2]) where [d. sub. 1] and [d. sub. 2] are defined as in Box 2. The importance of arbitrage in the pricing of options is clear. However, many option pricing models can be derived from the assumption of complete arbitrage. Each would differ according to the probability distribution of the price of the underlying asset. What makes the Black-Scholes model unique is that it assumes that stock prices are log-normally distributed, that is, that the logarithm of the stock price is normally distributed. This is often expressed in a â€Å"diffusion model† (see Box 2) in which the (instantaneous) rate of change in the stock price is the sum of two parts, a â€Å"drift,† defined as the difference between the expected rate of change in the stock price and the dividend yield, and â€Å"noise,† defined as a random variable with zero mean and constant variance. The variance of the noise is called the â€Å"volatility† of the stock's rate of price change. Thus, the rate of change in a stock price vibrates randomly around its expected value in a fashion sometimes called â€Å"white noise. † The Black-Scholes models of put and call option pricing apply directly to European options as long as a continuous dividend is paid at a constant rate. If no dividends are paid, the models also apply to American call options, which can be exercised at any time. In this case, it can be shown that there is no incentive for early exercise, hence the American call option must trade like its European counterpart. However, the Black-Scholes model does not hold for American put options, because these might be exercised early, nor does it apply to any American option (put or call) when a dividend is paid. (2) Our empirical analysis will sidestep those problems by focusing on European-style options, which cannot be exercised early. A call option's intrinsic value is defined as max(S – X,0), that is, the largest of S – X or zero; a put option's intrinsic value is max(X – S,0). When the stock price (S) exceeds a call option's strike price (X), or falls short of a put option's strike price, the option has a positive intrinsic value because if it could be immediately exercised, the holder would receive a gain of S – X for a call, or X – S for a put. However, if S [less than] X, the holder of a call will not exercise the option and it has no intrinsic value; if X [greater than] S this will be true for a put. The intrinsic value of a call is the kinked line in Figure 1 (a put's intrinsic value, not shown, would have the opposite kink). When the stock price exceeds the strike price, the call option is said to be in-the-money. It is out-of-the-money when the stock price is below the strike price. Thus, the kinked line, or intrinsic value, is the income from immediately exercising the option: When the option is out-of-the-money, its intrinsic value is zero, and when it is in the money, the intrinsic value is the amount by which S exceeds X. Convexity, the Call Premium, and the Greek Chorus The premium, or price paid for the option, is shown by the curved line in Figure 1. This curvature, or â€Å"convexity,† is a key characteristic of the premium on a call option. Figure 1 shows the relationship between a call option's premium and the underlying stock price for a hypothetical option having a 60-day term, a strike price of $50, and a volatility of 20 percent. A 5 percent riskless interest rate is assumed. The call premium has an upward-sloping relationship with the stock price, and the slope rises as the stock p rice rises. This means that the sensitivity of the call premium to changes in the stock price is not constant and that the option-replicating portfolio changes with the stock price. The convexity of option premiums gives rise to a number of technical concepts which describe the response of the premium to changes in the variables and parameters of the model. For example, the relationship between the premium and the stock price is captured by the option's Delta ([Delta]) and its Gamma ([Gamma]). Defined as the slope of the premium at each stock price, the Delta tells the trader how sensitive the option price is to a change in the stock price. (3) It also tells the trader the value of the hedging ratio. (4) For each share of stock held, a perfect hedge requires writing 1/[[Delta]. ub. c] call options or buying 1/[[Delta]. sub. p] puts. Figure 2 shows the Delta for our hypothetical call option as a function of the stock price. As S increases, the value of Delta rises until it reaches its maximum at a stock price of about $60, or $10 in-the-money. After that point, the option premium and the stock price have a 1:1 relationship. The increasing Delta also means that th e hedging ratio falls as the stock price rises. At higher stock prices, fewer call options need to be written to insulate the investor from changes in the stock price. The Gamma is the change in the Delta when the stock price changes. (5) Gamma is positive for calls and negative for puts. The Gamma tells the trader how much the hedging ratio changes if the stock price changes. If Gamma is zero, Delta would be independent of S and changes in S would not require adjustment of the number of calls required to hedge against further changes in S. The greater is Gamma, the more â€Å"out-of-line† a hedge becomes when the stock price changes, and the more frequently the trader must adjust the hedge. Figure 2 shows the value of Gamma as a function of the amount by which our hypothetical call option is in-the-money. (6) Gamma is almost zero for deep-in-the-money and deep-out-of-the-money options, but it reaches a peak for near-the-money options. In short, traders holding near-the-money options will have to adjust their hedges frequently and sizably as the stock price vibrates. If traders want to go on long vacations without changing their hedges, they should focus on far-away-from-the-money options, which have near-zero Gammas. A third member of the Greek chorus is the option's Lambda, denoted by [Lambda], also called Vega. (7) Vega measures the sensitivity of the call premium to changes in volatility. The Vega is the same for calls and puts having the same strike price and expiration date. As Figure 2 shows, a call option's Vega conforms closely to the pattern of its Gamma, peaking for near-the-money options and falling to zero for deep-out or deep-in options. Thus, near-the-money options appear to be most sensitive to changes in volatility. Because an option's premium is directly related to its volatility – the higher the volatility, the greater the chance of it being deep-in-the-money at expiration – any propositions about an option's price can be translated into statements about the option's volatility, and vice versa. For example, other things equal, a high volatility is synonymous with a high option premium for both puts and calls. Thus, in many contexts we can use volatility and premium interchangeably. We will use this result below when we address an option's implied volatility. Other Greeks are present in the Black-Scholes pantheon, though they are lesser gods. The option's Rho ([Rho]) is the sensitivity of the call premium to changes in the riskless interest rate. (8) Rho is always positive for a call (negative for a put) because a rise in the interest rate reduces the present value of the strike price paid (or received) at expiration if the option is exercised. The option's Theta ([Theta]) measures the change in the premium as the term shortens by one time unit. (9) Theta is always negative because an option is less valuable the shorter the time remaining. The Black-Scholes Assumptions The assumptions underlying the Black-Scholes model are few, but strong. They are: * Arbitrage: Traders can, and will, eliminate any arbitrage profits by simultaneously buying (or writing) options and writing (or buying) the option-replicating portfolio whenever profitable opportunities appear. * Continuous Trading: Trading in both the option and the underlying security is continuous in time, that is, transactions can occur simultaneously in related markets at any instant. * Leverage: Traders can borrow or lend in unlimited amounts at the riskless rate of interest. Homogeneity: Traders agree on the values of the relevant parameters, for example, on the riskless rate of interest and on the volatility of the returns on the underlying security. * Distribution: The price of the underlying security is log-normally distributed with statistically independent price changes, and with constant mean and constant variance. * Continuous Prices: No discontinuous jumps occur in the price of the underlying security. * Transactions Costs: The cost of engaging in arbitrage is negligibly small. The arbitrage assumption, a fundamental proposition in economics, has been discussed above. The continuous trading assumption ensures that at all times traders can establish hedges by simultaneously trading in options and in the underlying portfolio. This is important because the Black-Scholes model derives its power from the assumption that at any instant, arbitrage will force an option's premium to be equal to the value of the replicating portfolio. This cannot be done if trading occurs in one market while trading in related markets is barred or delayed. For example, during a halt in trading of the underlying security one would not expect option premiums to conform to the Black-Scholes model. This would also be true if the underlying security were inactively traded, so that the trader had â€Å"stale† information on its price when contemplating an options transaction. The leverage assumption allows the riskless interest rate to be used in options pricing without reference to a trader's financial position, that is, to whether and how much he is borrowing or lending. Clearly this is an assumption adopted for convenience and is not strictly true. However, it is not clear how one would proceed if the rate on loans was related to traders' financial choices. This assumption is common to finance theory: For example, it is one of the assumptions of the Capital Asset Pricing Model. Furthermore, while private traders have credit risk, important players in the option markets, such as nonfinancial corporations and major financial institutions, have very low credit risk over the lifetime of most options (a year or less), suggesting that departures from this assumption might not be very important. The homogeneity assumption, that traders share the same probability beliefs and opportunities, flies in the face of common sense. Clearly, traders differ in their judgments of such important things as the volatility of an asset's future returns, and they also differ in their time horizons, some thinking in hours, others in days, and still others in weeks, months, or years. Indeed, much of the actual trading that occurs must be due to differences in these judgments, for otherwise there would be no disagreements with â€Å"the market† and financial markets would be pretty dull and uninteresting. The distribution assumption is that stock prices are generated by a specific statistical process, called a diffusion process, which leads to a normal distribution of the logarithm of the stock's price. Furthermore, the continuous price assumption means that any changes in prices that are observed reflect only different draws from the same underlying log-normal distribution, not a change in the underlying probability distribution itself. II. Tests of the Black-Scholes Model. Assessments of a model's validity can be done in two ways. First, the model's predictions can be confronted with historical data to determine whether the predictions are accurate, at least within some statistical standard of confidence. Second, the assumptions made in developing the model can be assessed to determine if they are consistent with observed behavior or historical data. A long tradition in economics focuses on the first type of tests, arguing that â€Å"the proof is in the pudding. It is argued that any theory requires assumptions that might be judged â€Å"unrealistic,† and that if we focus on the assumptions, we can end up with no foundations for deriving the generalizations that make theories useful. The only proper test of a theory lies in its predictive ability: The theory that consistently predicts best is the best theory, regardless of the assumptions required to generate the theory. Tests based on assumptions are justified by the principle of â€Å"garbag e in-garbage out. † This approach argues that no theory derived from invalid assumptions can be valid. Even if it appears to have predictive abilities, those can slip away quickly when changes in the eThe Data The data used in this study are from the Chicago Board Options Exchange's Market Data Retrieval System. The MDR reports the number of contracts traded, the time of the transaction, the premium paid, the characteristics of the option (put or call, expiration date, strike price), and the price of the underlying stock at its last trade. This information is available for each option listed on the CBOE, providing as close to a real-time record of transactions as can be found. While our analysis uses only records of actual transactions, the MDR also reports the same information for every request of a quote. Quote records differ from the transaction records only in that they show both the bid and asked premiums and have a zero number of contracts traded. nvironment make the invalid assumptions more pivotal. The data used are for the 1992-94 period. We selected the MDR data for the S&P 500-stock index (SPX) for several reasons. First, the SPX options contract is the only European-style stock index option traded on the CBOE. All options on individual stocks and on other indices (for example, the S&P 100 index, the Major Market Index, the NASDAQ 100 index) are American options for which the Black-Scholes model would not apply. The ability to focus on a European-style option has several advantages. By allowing us to ignore the potential influence of early exercise, a possibility that significantly affects the premiums on American options on dividend-paying stocks as well as the premiums on deep-in-the-money American put options, we can focus on options for which the Black-Scholes model was designed. In addition, our interest is not in individual stocks and their options, but in the predictive power of the Black-Scholes option pricing model. Thus, an index option allows us to make broader generalizations about model performance than would a select set of equity options. Finally, the S&P 500 index options trade in a very active market, while options on many individual stocks and on some other indices are thinly traded. The full MDR data set for the SPX over the roughly 758 trading days in the 1992-94 period consisted of more than 100 million records. In order to bring this down to a manageable size, we eliminated all records that were requests for quotes, selecting only records reflecting actual transactions. Some of these transaction records were cancellations of previous trades, for example, trades made in error. If a trade was canceled, we included the records of the original transaction because they represented market conditions at the time of the trade, and because there is no way to determine precisely which transaction was being canceled. We eliminated cancellations because they record the S&P 500 at the time of the cancellation, not the time of the original trade. Thus, cancellation records will contain stale prices. This screening created a data set with over 726,000 records. In order to complete the data required for each transaction, the bond-equivalent yield (average of bid and asked prices) on the Treasury bill with maturity closest to the expiration date of the option was used as a riskless interest rate. These data were available for 180-day terms or less, so we excluded options with a term longer than 180 days, leaving over 486,000 usable records having both CBOE and Treasury bill data. For each of these, we assigned a dividend yield based on the S&P 500 dividend yield in the month of the option trade. Because each record shows the actual S&P 500 at almost the same time as the option transaction, the MDR provides an excellent basis for estimating the theoretically correct option premium and evaluating its relationship to actual option premiums. There are, however, some minor problems with interpreting the MDR data as providing a trader's-eye view of option pricing. The transaction data are not entered into the CBOE computer at the exact moment of the trade. Instead, a ticket is filled out and then entered into the computer, and it is only at that time that the actual level of the S&P 500 is recorded. In short, the S&P 500 entries necessarily lag behind the option premium entries, so if the S&P 500 is rising (falling) rapidly, the reported value of the SPX will be above (below) the true value known to traders at the time of the transaction Test 1: An Implied Volatility Test A key variable in the Black-Scholes model is the volatility of returns on the underlying asset, the SPX in our case. Investors are assumed to know the true standard deviation of the rate of return over the term of the option, and this information is embedded in the option premium. While the true volatility is an unobservable variable, the market's estimate of it can be inferred from option premiums. The Black-Scholes model assumes that this â€Å"implied volatility† is an optimal forecast of the volatility in SPX returns observed over the term of the option. The calculation of an option's implied volatility is reasonably straightforward. Six variables are needed to compute the predicted premium on a call or put option using the Black-Scholes model. Five of these can be objectively measured within reasonable tolerance levels: the stock price (S), the strike price (X), the remaining life of the option (T – t), the riskless rate of interest over the remaining life of the option (r), typically measured by the rate of interest on U. S. Treasury securities that mature on the option's expiration date, and the dividend yield (q). The sixth variable, the â€Å"volatility† of the return on the stock price, denoted by [Sigma], is unobservable and must be estimated using numerical methods. Using reasonable values of all the known variables, the implied volatility of an option can be computed as the value of [Sigma] that makes the predicted Black-Scholes premium exactly equal to the actual premium. An example of the computation of the implied volatility on an option is shown in Box 3. The Black-Scholes model assumes that investors know the volatility of the rate of return on the underlying asset, and that this volatility is measured by the (population) standard deviation. If so, an option's implied volatility should differ from the true volatility only because of random events. While these discrepancies might occur, they should be very short-lived and random: Informed investors will observe the discrepancy and engage in arbitrage, which quickly returns things to their normal relationships. Figure 3 reports two measures of the volatility in the rate of return on the S&P 500 index for each trading day in the 1992-94 period. (10) The â€Å"actual† volatility is the ex post standard deviation of the daily change in the logarithm of the S&P 500 over a 60-day horizon, converted to a percentage at an annual rate. For example, for January 5, 1993 the standard deviation of the daily change in lnS&P500 was computed for the next 60 calendar days; this became the actual volatility for that day. Note that the actual volatility is the realization of one outcome from the entire probability distribution of the standard deviation of the rate of return. While no single realization will be equal to the â€Å"true† volatility, the actual volatility should equal the true volatility, â€Å"on average. † The second measure of volatility is the implied volatility. This was constructed as follows, using the data described above. For each trading day, the implied volatility on call options meeting two criteria was computed. The criteria were that the option had 45 to 75 calendar days to expiration (the average was 61 days) and that it be near the money (defined as a spread between S&P 500 and strike price no more than 2. 5 percent of the S&P 500). The first criterion was adopted to match the term of the implied volatility with the 60-day term of the actual volatility. The second criterion was chosen because, as we shall see later, near-the-money options are most likely to conform to Black-Scholes predictions. The Black-Scholes model assumes that an option's implied volatility is an optimal forecast of the volatility in SPX returns observed over the term of the option. Figure 3 does not provide visual support for the idea that implied volatilities deviate randomly from actual volatility, a characteristic of optimal forecasting. While the two volatility measures appear to have roughly the same average, extended periods of significant differences are seen. For example, in the last half of 1992 the implied volatility remained well above the actual volatility, and after the two came together in the first half of 1993, they once again diverged for an extended period. It is clear from this visual record that implied volatility does not track actual volatility well. However, this does not mean that implied volatility provides an inferior forecast of actual volatility: It could be that implied volatility satisfies all the scientific requirements of a good forecast in the sense that no other forecasts of actual volatility are better. In order to pursue the question of the informational content of implied volatility, several simple tests of the hypothesis that implied volatility is an optimal forecast of actual volatility can be applied. One characteristic of an optimal forecast is that the forecast should be unbiased, that is, the forecast error (actual volatility less implied volatility) should have a zero mean. The average forecast error for the data shown in Figure 3 is -0. 7283, with a t-statistic of -8. 22. This indicates that implied volatility is a biased forecast of actual volatility. A second characteristic of an optimal forecast is that the forecast error should not depend on any information available at the time the forecast is made. If information were available that would improve the forecast, the forecaster should have already included it in making his forecast. Any remaining forecasting errors should be random and uncorrelated with information available before the day of the forecast. To implement this â€Å"residual information test,† the forecast error was regressed on the lagged values of the S&P 500 in the three days prior to the forecast. 11) The F-statistic for the significance of the regression coefficients was 4. 20, with a significance level of 0. 2 percent. This is strong evidence of a statistically significant violation of the residual information test. The conclusion that implied volatility is a poor forecast of actual volatility has been reached in several other studies using different methods and data. For example, Canina and Figlewski (1993), using data for the S&P 100 in the years 1983 to 1987, found that implied volatility had almost no informational content as a prediction of actual volatility. However, a recent review of the literature on implied volatility (Mayhew 1995) mentions a number of papers that give more support for the forecasting ability of implied volatility. Test 2: The Smile Test One of the predictions of the Black-Scholes model is that at any moment all SPX options that differ only in the strike price (having the same term to expiration) should have the same implied volatility. For example, suppose that at 10:15 a. m. on November 3, transactions occur in several SPX call options that differ only in the strike price. Because each of the options is for the same interval of time, the value of volatility embedded in the option premiums should be the same. This is a natural consequence of the fact that the variability in the S&P 500's return over any future period is independent of the strike price of an SPX option. One approach to testing this is to calculate the implied volatilities on a set of options identical in all respects except the strike price. If the Black-Scholes model is valid, the implied volatilities should all be the same (with some slippage for sampling errors). Thus, if a group of options all have a â€Å"true† volatility of, say, 12 percent, we should find that the implied volatilities differ from the true level only because of random errors. Possible reasons for these errors are temporary deviations of premiums from equilibrium levels, or a lag in the reporting of the trade so that the value of the SPX at the time stamp is not the value at the time of the trade, or that two options might have the same time stamp but one was delayed more than the other in getting into the computer. This means that a graph of the implied volatilities against any economic variable should show a flat line. In particular, no relationship should exist between the implied volatilities and the strike price or, equivalently, the amount by which each option is â€Å"in-the-money. † However, it is widely believed that a â€Å"smile† is present in option prices, that is, options far out of the money or far in the money have higher implied volatilities than near-the-money options. Stated differently, deep-out and far-in options trade â€Å"rich† (overpriced) relative to near-the-money options. If true, this would make a graph of the implied volatilities against the value by which the option is in-the-money look like a smile: high implied volatilities at the extremes and lower volatilities in the middle. In order to test this hypothesis, our MDR data were screened for each day to identify any options that have the same characteristics but different strike [TABULAR DATA FOR TABLE 1 OMITTED] prices. If 10 or more of these â€Å"identical† options were found, the average implied volatility for the group was computed and the deviation of each option's implied volatility from its group average, the Volatility Spread, was computed. For each of these options, the amount by which it is in-the-money was computed, creating a variable called ITM (an acronym for in-the-money). ITM is the amount by which an option is in-the-money. It is negative when the option is out-of-the-money. ITM is measured relative to the S&P 500 index level, so it is expressed as a percentage of the S&P 500. The Volatility Spread was then regressed against a fifth-order polynomial equation in ITM. This allows for a variety of shapes of the relationship between the two variables, ranging from a flat line if Black-Scholes is valid (that is, if all coefficients are zero), through a wavy line with four peaks and troughs. The Black-Scholes prediction that each coefficient in the polynomial regression is zero, leading to a flat line, can be tested by the F-statistic for the regression. The results are reported in Table 1, which shows the F-statistic for the hypothesis that all coefficients of the fifth-degree polynomial are jointly zero. Also reported is the proportion of the variation in the Volatility Spreads, which is explained by variations in ITM ([R. sup. 2]). The results strongly reject the Black-Scholes model. The F-statistics are extremely high, indicating virtually no chance that the value of ITM is irrelevant to the explanation of implied volatilities. The values of [R. sup. 2] are also high, indicating that ITM explains about 40 to 60 percent of the variation in the Volatility Spread. Figure 4 shows, for call options only, the pattern of the relationship between the Volatility Spread and the amount by which an option is in-the-money. The vertical axis, labeled Volatility Spread, is the deviation of the implied volatility predicted by the polynomial regression from the group mean of implied volatilities for all options trading on the same day with the same expiration date. For each year the pattern is shown throughout that year's range of values for ITM. While the pattern for each year looks more like Charlie Brown's smile than the standard smile, it is clear that there is a smile in the implied volatilities: Options that are further in or out of the money appear to carry higher volatilities than slightly out-of-the-money options. The pattern for extreme values of ITM is more mixed. Test 3: A Put-Call Parity Test Another prediction of the Black-Scholes model is that put options and call options identical in all other respects should have the same implied volatilities and should trade at the same premium. This is a consequence of the arbitrage that enforces put-call parity. Recall that put-call parity implies [P. sub. t] + [e. sup. -q(T – t)][S. sub. t] = [C. sub. t] + [Xe. sup. -r(T – t)]. A put and a call, having identical strike prices and terms, should have equal premiums if they are just at-the-money in a present value sense. If, as this paper does, we interpret at-the-money in current dollars rather than present value (that is, as S = X rather than S = [Xe. sup. -r(t – q)(T – t)]), at-the-money puts should have a premium slightly below calls. Because an option's premium is a direct function of its volatility, the requirement that put premiums be no greater than call premiums for equivalent at-the-money options implies that implied volatilities for puts be no greater than for calls. For each trading day in the 1992-94 period, the difference between implied volatilities for at-the-money puts and calls having the same expiration dates was computed, using the [+ or -]2. 5 percent criterion used above. (12) Figure 5 shows this difference. While puts sometimes have implied volatility less than calls, the norm is for higher implied volatilities for puts. Thus, puts tend to trade â€Å"richer† than equivalent calls, and the Black-Scholes model does not pass this put-call parity test.

Age Discrimination in the Workplace - Free Essay Example

Sample details Pages: 8 Words: 2362 Downloads: 6 Date added: 2019/04/01 Category Society Essay Level High school Tags: Discrimination Essay Did you like this example? In this current time, age discrimination has increased reported incidents around the world. For many, this type of discrimination is hampering the rights of employees or artists who were given less priority to claim a certain privilege or opportunity. This is due to the preference of institutions and groups towards the younger generation who are more active and cooperative. Don’t waste time! Our writers will create an original "Age Discrimination in the Workplace" essay for you Create order The aging population is concerned that this type of discrimination can significantly affect their emotional integrity due to the intimidating factors caused by stereotyping companies. However, there are new laws that are attempting to prevent this situation from happening in the future. Companies are usually apprehended after finding out that they have been involved in discriminative nature towards individuals who are subjected for apprehension. Now, the law of discrimination is slowly gaining presence around the world due to its negative impact to the community with the elderly population (Prokurat, 2012). As for the case of Liebman v. Metropolitan Life Insurance, the issue is all about discrimination on the basis of age. Employees were restricted from receiving benefits as well as having the threat on discontinuing their employment contract based on their older age group. As a result, their financial and emotional well-being is compromised due to the fact that elderly employees are already singled out by the company. The main goal is to employ younger individuals who are more technically skilled and does not have any medical conditions to continue the business efficiently. As a response to this application, limiting the aging workforce has been generating a concern to the community due to the unfair treatment of Metropolitan Life Insurance. Although there are laws prohibiting age discrimination, this law is not totally implemented as it causes an individual to become degraded with a privilege on working with the company whom does not want older employees. Argument Older employees have benefits to claim, which are the insurances and job opportunities since they are still productive as the younger population. The most interesting fact in this feature is their ability to comprehend simple tasks provided by the company, old school etiquette. This is because there are new laws that provide multiple privileges for the elderly to receive benefits as they have already contributed significant productive practices in the community. Instances of age-related discrimination are often managed by the Supreme Court if there are brave elderly employees who managed to file for a legal lawsuit against their employer. The involvement of the Supreme Court generates public attention due to the nature of the lawsuit that provides a wake-up call for companies to start considering age discrimination to become more affiliated with equal treatment. Example is when an employee is fired due to a health condition, which attracts the attention of the authorities. Elderly employees are understandably weaker as compared to the younger generation. Employees aged 65 and above are mostly experiencing health problems due to a poor sedentary lifestyle that limits their physical and mental performance. They may need to work longer due to lack of retirement income and limited insurance coverages. This is usually the basis of some companies who has been considering if they are still capable of accomplishing several tasks as applied by the managers. The only problem is when elderly becomes sick; it can significantly halt the operation of their performance, leading to a disruption or delays with a certain process. The impact of traditional common law constraints on managers could risk the companys productivity at a given time and strain the performance of the company if the company still employs an individual who is already suffering from discomforts at a given time. For example, a 72-year-old employee, employed with the company for 29 years, with a seco nd diagnosis of breast cancer. With treatments and surgeries, she returns to work from FMLA and Short-Term Disability, however, she is tired and still healing. She is unable to handle the day-to-day responsibilities. With modifications to her workload, how long does the employer continue paying her wages when like employees have additional workloads? From the negative point of view from employers as managers, they believe that it is the right choice for elderly employees to have a limited employment contract. The main reason is that there are physical, mental, and emotional limitations associated with the skills and knowledge performance. As managers, the welfare of the company is always considered as an important consideration to ensure that the level of productivity is competent. Managers are aware that there are corresponding benefits awaiting elderly employees, which is beneficial as compared to the younger generation. This reason has been considering civil rights groups concerned due to the risk of disconnecting affected employees from having a productive socio-economic lifestyle. As a result, there is a surge in seniors filing bankruptcy according to the Washington Post (Singletary, 2018). There is a higher cost of living and Social Security alone will not sustain their debt without a supplemental income. With response to the legal laws, the Equal Employment Opportunities Commission (EEOC) is an institution that regulates all issues of age-related discrimination within the workplace. It conducts investigation to companies involved with complaints of discriminating their employees based on their age. The Age Discrimination Employment Act of 1967 (ADEA) is another law that aims to sanction any company, individual, or group involved in segregating other individuals basing on their age, race, color, nationality, or gender (Clarkson, 2012). There are corresponding punishments and financial liabilities for individuals who have been proven on committing discrimination against other individuals or even groups. For Older Worker Benefits Protection Act (OWBPA), it aims to provide more benefits for the elderly population to have more opportunities to maintain their productive socio-economic status. By reaching the age of 65, there are several benefits and compensation opportunities that are prov ided to them regardless of the company they are currently affiliated to in order to prevent any risk of committing financial losses (Echavarria, 2015). Definition of Age Discrimination When we say age discrimination, this is the process wherein an individual has been degraded with their right or privilege basing on their age aside from their health conditions. This scenario usually occurs at the workplace wherein the affected employee has been regarded as a liability by the company or an individual responsible for discriminating an employee. At first, the affected employee may not realize that their age is the factor for influencing their employment viability. However, they become emotionally frustrated whenever discovering that they have been segregated due to their age. Therefore, there are laws such as the ADEA who can be responsible for monitoring companies involved in these illegal acts towards elderly for a possibility of apprehension. As a result, it seeks to protect all affected employees who are at risk from being laid off from the company they are currently working. Jobs that restrict older employees One major job example is a pilot because airline companies are always ensuring the full safety of their passengers while traversing in mid-air. Pilots who are already old are required not to continue their service, especially if they failed recent vision testing. The visual acuity of people who are already in their elder stage can be observed having a blurred vision. Health care employees such as doctors and nurses are also required to retire at the age of 65, in which their physical capacity may no longer sustain their productive skills and knowledge at the workplace. As a physician or nurse, working in the health care facility require physical demand that elders cannot sustain while at work. In this case, there are several factors such as acquiring nosocomial infection, accidents, and the risk of injury that other health care practitioners are not considering any lawsuits from the employees (Skelsey, 2013). Protection from discrimination Employers can protect themselves from any risk of facing discriminative nature by means of complying with the law. Every employer must understand that there are EEOC applications that monitor companies from any risk of being involved in discriminatory practices. These are institutions who are involved in illegal practice for excluding their employees based on their age. The ADEA is a provision wherein companies or any involved employer will be subjected to legal proceedings once there are strong evidences that they have been involved in such practices. It prohibits employers from forcing these aged employees into retirement if they are in nonmanagerial positions with other criteria (Clarkson, 2012). For OWBPA, employers are raising compensation and benefits for elderly individuals on the basis that they can become a role model to other companies who provides higher benefits for older employees. The 72-year-old employee with breast cancer should be provided accommodations to her hards hip (Clarkson, 2012) which may require modifying her job responsibilities to avoid a potential discrimination claim. Hiring practices With the issue against existing employees on the basis of retirement age, there are certain practices wherein employees are indeed needed to resign after several years of serving the company. One of the most critical issues is the separation pay because when there is a forced retirement for employees, it is important for the companies to pay their separation wage. In this case, employees will have a financial bridge to establish a business after their professional career demise. This employers right is regarded as the appoint or dismissal of an employee. This is in accordance to the procedures that complies with the laws to prevent any risk of being discriminated. Furthermore, employers have the rightful decision to protect the interest of their brand or companys image from any intent to abuse their authority just to conduct illicit activities against the company (Perry, 2014). For employees over forced retirement indicates that the employer may have the right to protect its own image and reputation from any unjust practices made by the employee. This is because there are conditions wherein the employee may have caught doing illegal or unacceptable practices that promoted harm and intimidation to other employees. When the employee becomes violent enough to cause a significant damage to the company, forced resignation or retirement regardless of age is allowed under the law. In terms of health-related problems, the company may also have the right to ensure that the employee should undergo an immediate rehabilitation. This is regardless of age wherein the employee should not be exerting more effort as their health might be further compromised if they continue to render their service with the company (Levin, 2012) As for employees who were terminated, the main basis is their behavioral characteristics while working with the company. In favor of employers rights, they have always the prerogative to take action against an employee who was caught involved in activities that violates the rules of the company. Sometimes, there are company laws that restrict employees from performing something that has been causing a conflict of interest with the company. One example is when the employee is caught involved in fraudulent transactions that gravely affected the practices of the whole institution. Therefore, the employers believe that they are not going anything wrong because they are just protecting their company and the safety of all other employees of the company (Tuccille, 2012). Employers have the right to terminate their employee if there are scenarios that are considered before deciding to discontinue their working contracts. The first provision is that when the contract has been breached, the em ployer has the right to let go of the employee regardless of the age, race gender, or nationality. Possible effects of employees within the workplace The first possible impacts of having an older employee within the workplace are the presence of seniority, in which the main goal is to impose an autocratic ambiance to promote discipline within the community or society. Younger employees will learn how to become respectful with other employees, which is responsible for promoting an optimistic camaraderie between two or more employees and other stakeholders. This is all about controlling behaviors that are usually presumed inappropriate under the eyes of elder employees. The second effect is to create balanced behavioral instincts that regulate the attitudes of different personalities in the workplace. Older employees always promote harmonious working relationships with the younger employees by means of sharing their experiences that are useful to prevent any dangerous remarks affecting the integrity of the company. Having a good camaraderie enhances harmonious working relationships because older employees share their knowledge regar ding their past experiences with similar situation. In the case mentioned earlier of the employee returning from bouts of breast cancer, she never abused her time and filed for her benefits accordingly. This sets an example to the younger team members of time management. This is because older employees have already experienced certain practices that are important for the companys implementation of laws such as age discrimination that affects the security of tenure in their respective job. These older employees are able to provide previous reactions from other individuals who encountered discriminatory actions from their past employment experience (Evans, 2012). It has been learned that age discrimination is a serious offense that can be committed by an individual or group at work. This is solely based on the prejudiced actions applied by the involved offending party towards the victim that has been enduring the agony of suffering from mistreatment. Managers are aware that age discrimination can bring a significant threat to the psychological capacity of an affected individual. The main reason behind this is that employees can be terminated without prior notice, making them financially unstable after the incident. The best solution is to analyze the existing laws applied by the company to discover some rules that promotes prejudiced practices towards the rights of employees. For this reason, there are plenty of times that a person can change the existing law and implementation made by the operating institution with respect to the rights of their employees in compliance with the laws such as EEOC, ADEA and OWBPA (Watkins, 2011). Doing the rig ht thing to ensure the quality of our employees should be a priority in all business structures. Creating an open communication structure will equip managers with the understanding of where the senior employees are in their careers and to support their need to continue working under the circumstances or to assist if one makes the decision to face retirement.

AJU Admisions Tuition, Financial Aid, Enrollment...

While AJU does not require students to submit test scores from the SAT or ACT for admissions, students can submit these scores if they are interested in some of the scholarships offered by the school. To apply, students must submit an application, a high school transcript, and a letter of recommendation. Students can either submit an application with the school, or use the Common Application. In addition, applicants have the option to submit a second letter of recommendation, and they can set up an interview with an admissions counselor. Admissions Data (2016): American Jewish University Acceptance Rate: 60 percentAmerican Jewish University  has test-optional admissionsTest Scores -- 25th / 75th PercentileSAT Critical Reading: - / -SAT Math: - / -SAT Writing: - / -Whats a good SAT score?ACT Composite: - / -ACT English: - / -ACT Math: - / -Whats a good ACT score? American Jewish UniversityDescription: In 2007, the University of Judaism and the  Brandeis-Bardin Institute merged, creating the American Jewish University. Located in Los Angeles, California, AJU provides degree programs at the undergraduate and graduate levels. At the Whizin Center for Continuing Education, students of all ages may take courses in a range of subjects; while these courses carry no credits, they are taken for edification and enjoyment.   With art galleries, extensive libraries, sculpture gardens, performing art spaces, and a number of student activities, there is something for everyone to enjoy and learn from at AJU. Home to roughly 200 students, AJU boasts an impressive student / faculty ratio of 4  to 1. Dedicated to teaching and sharing Judaism, AJU offers a five-year training program at the Ziegler School of Rabbinic Studies; AJU is also affiliated with and oversees Camp Alonim and Gan Alonim Day Camp--two camps that allow children of all ages to explore and learn about the Jewish faith and traditions. Enrollment (2016): Total Enrollment: 159  (65 undergraduates)Gender Breakdown: 46 percent male / 54 percent female94  percent full-time Costs (2016- 17): Tuition and Fees: $30,338Books: $1,791  (why so much?)Room and Board: $16,112Other Expenses: $3,579Total Cost: $51,820 American Jewish UniversityFinancial Aid (2015- 16): Percentage of New Students Receiving Aid: 82 percentPercentage of New Students Receiving Types of AidGrants: 82 percentLoans: 55 percentAverage Amount of AidGrants: $10,899Loans: $6,760 Academic Programs: Most Popular Majors:  Psychology, Business Management, Biology, Philosophy and Religious Studies, Theology Transfer, Graduation and Retention Rates: First Year Student Retention (full-time students): 88 percent4-Year Graduation Rate: 31 percent6-Year Graduation Rate: 44 percent Data Source: National Center for Educational Statistics If You Like American Jewish University, You May Also Like These Schools: For students interested in a college founded in Judaism, other options in the country include Touro College and List College (Jewish Theological Seminary of America), both located in New York City. If youre looking for a small (less than 1,000 students) school on the west coast with an academic or religious focus, Holy Names University, Columbia College Hollywood, Soka University of America, and Warner Pacific College are all good options to consider.    AJU and the Common Application American Jewish University uses the  Common Application. These articles can help guide you: Common Application essay tips and samplesShort answer tips and samplesSupplemental essay tips and samples