...Queensland, Australia 4111 Abstract Following the introduction of the European Union Emissions Trading Scheme, CO2 emissions have become a tradable commodity. As a regulated party, emitters are forced to take into account the additional carbon emissions costs in their production costs structure. Given the high volatility of carbon price, the importance of price risk management becomes unquestioned. This study is the first attempt to calculate hedge ratios and to investigate their hedging effectiveness in the EU-ETS carbon market by applying conventional and recently developed models of estimation. These hedge ratios are then compared with those derived for other markets. In spite of the uniqueness and novelty of the carbon market, the results of the study are consistent with those found in other markets – that the hedge ratio is in the range of 0.5 to 1.0 and still best estimated by simple regression models. Key words: hedging, conditional hedge ratio; carbon market; CO2; emissions trading; risk management JEL classification: G32, G19, Q54, C32 1 1. Introduction Following the introduction of the European Union Emissions Trading Scheme (EU-ETS) in early 2005, emissions trading...
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... density | 6000 | .99 | 1.04 | .9949 | .00504 | pH | 6000 | 2.74 | 4.01 | 3.2195 | .16022 | sulphates | 6000 | .2200 | 2.0000 | .532073 | .1487300 | alcohol | 6000 | 8.0000 | 14.9000 | 10.491008 | 1.1901957 | White | 6000 | 0 | 1 | .75 | .433 | Some of our variables in the dataset have very tight ranges, for example density has a min of .99 and a max of 1.04. On the other hand, total sulfur dioxide has a range of 6 to 440 and a standard deviation of 56.6, with the max value being over 5 standard deviations from the mean. In order to get a sense of the overall dataset, we ran a regression of all of the original variables with the dependent variable of quantity. Below are the outputs of the regression (Potential Model 1): Potential Model 1 Summary | R Square | Adjusted R Square |...
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...below expectations) through a combination of recovering market conditions, a new era of smart products and with official interest rates likely to be placed on hold by the RBA until the end of 2011. (A) CHOICE OF MODELS ------------------------------------------------- 1. Discount Models Why FCFF Discount Model? DDM would not be a suitable model because JBH paid dividends which are significantly greater than or lower than FCFE to the firm between 2006 and 2010 thereby underestimating or overestimating the value of JBH (dividends less than 80% of FCFE or greater than 110% FCFE) . The debt to equity ratio has been volatile declining from 82.90% in 2003 to 23.73% in 2010 with a spike of 120.96% in 2006. Estimating future debt issues and repayments will prove to be difficult given that changes are expected because JBH has raised their senior debt facility by $105 million expiring by 2014 possibly to finance the roll out of up to 193 new stores by 2014 as well. The recent stock repurchases of $173 million and possible future repurchases if JBH continues to accumulate cash, will also have significant impacts on leverage. FCFF will certainly be the most appropriate model to apply since debt is not directly considered in determining the cash flow whereas in the FCFE model, the value of net debt issued must be backed out. Furthermore, FCFF refers to cash flows available to investors so recent stock repurchase of $173...
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...12 Course Project AJ DAVIS DEPARTMENT STORES PROJECT PART A In this course project, my aim is to present the statistical analysis of the data for Aj Davis departmental store chain, which has many credit customers and wants to find out more information about these customers. In analyzing the individual variable, using graphical illustrations I would be using histogram, bar chart and a pie chart because there are useful when using numerical comparison. The 3 individual variables 1) The 1st individual variable, I choose is the credit balance of the customers. Credit Balance Numerical Summary: | Credit Balance($) | Mean | 3970 | Median | 4090 | Mode | 3890 | Standard Deviation | 932 | Skewness | -0.15 | Range | 3814 | Minimum | 1864 | Sum | 198523 | Maximum | 5678 | Interpretation The mean credit balance of the customers is given as $3970. The standard deviation is given approximately as 932. The credit balance of the customers is more or less normally distributed with the peak of the bell shaped distribution lying in the range 3814. 2) The 2nd individual variable, I choose is the household size. Thus the number of people living in the households. SIZE Frequency Distribution | Size | Frequency | 1 | 5 | 2 | 15 | 3 | 8 | 4 | 9 | 5 | 5 | 6 | 5 | 7 | 3 | Interpretation The mean of the household size is known as 3 approximately as seen on Minitab. The median of the household size 3 and the mode would be 2. As...
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...understanding in what really affects the credit balances in AJ department stores we need to do a regression analysis to help us get to 100% confidence in making the right decisions. We did a scatter plot to help us understand the relationship between credit balance and size and we found that we can see there is a direct relationship with credit balance. We can expect a decrease of -2.3 and we can see that an increase of .14% in size. We were able to Determine the coefficient of correlation r= 0.753 which means that we have a strong direct linear relationship between size and credit balance. We also found that 56.68% of the variability in credit balance is explained by size in the house hold through our index of determination. We also test the t value and found that we were 100% sure that the size of house hold can be used to predict credit balance. But just using this data we are not convince there is a correlation between credit balance and size, so we did some predictions to find a better understanding. By being only 95% We are confident that the size of household will be in average of credit balance will be 0.00105 and 0.001758. Than we wanted to know the average so We are confident that the size of household will be in average of credit balance will be 0.00105 and 0.001758. Than we did a prediction with a household of 5 and we are sure 95% sure that the household size of 5 will be in average of credit balance of -3.57908 to -0.685750. With our prediction We are 95% sure that...
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...IEE 572 Term Project EXECUTIVE SUMMARY The purpose of the experiment is to analyze factors that affect the texture of the Rice. We chose to conduct the experiment with 4 factors which were thought to be the most important ones. Each factor is run at two levels so that the experiment is a 24 factorial design with two replicates. The experiment runs were randomized and the results were analyzed using Design Expert Software. This software helped us in identifying the main effects. Then the design was projected in the significant effects to get further insight into the factors affecting the texture of rice The findings of this experiment are elaborated in the conclusion section. OBJECTIVE • To identify the factors affecting the texture of rice. • To come out with a clear recommendation regarding the most favorable combination that gives rice a better texture. DESIGN OF THE EXPERIMENT Initial Design Phase The experiment design details are as below: Choice of factors • Potential design factors After careful analysis of various factors that may effect the texture, four factors were short...
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...CFD ANALYSIS AND SIMULATION OF SHOCKWAVE GENERATION Dr. S. P. Vendan #1, K. A. Mohamed Isqhak*2, R. Prabakaran *3, N.Sugajen*4 and M.Pravinkumar*5 #1 Associate Professor, Department of Mechanical Engineering, PSG College of Technology, Coimbatore-641004. 1 spvendann@yahoo.com * IV Year BE Mechanical Engineering, Department of Mechanical Engineering, PSG College of Technology, Coimbatore-641004. 2 mailmepravin@gmail.com 3 prbkrn1991@gmail.com Abstract. This paper involves computational fluid dynamics analysis and simulation of a shock wave generating equipment. Shock waves are produced by suddenly exposing a high pressure region to a low pressure region. This involves in the design of an arrangement that acts as a valve which separates high pressure region and low pressure region such that the valve opens suddenly (i.e., in the order of milliseconds) and thus producing shock waves. The instantaneous rise in pressure and temperature of a medium can be used in a variety of industrial applications Key words: Shock waves, Shock tubes, CFD, Pneumatic Valve 1 Introduction The ability of shock waves to instantaneously increase the pressure and temperature in a medium of propagation enables their use for many novel industrial applications[1]. In some sense the presence of a shockwave propagating in an enclosed medium can be similar to a furnace where, in addition to temperature, even pressure can go up instantaneously and remain at elevated levels for a short time and...
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...Session #1: Fundamental Analysis and Valuation March 2015 In-Mu Haw (许 仁茂) 1 Create value through acquisition to build brands (over 100) 2 Lenovo vs. HP Stock Price Lenovo created value through acquisitions Poor acquisition (overpaid: $8.8B) $18 million in 2013 3 Deloitte Report Chet Wood, Managing Partner of Deloitte LLP, Merger & Acquisition Services: • • About 70 percent of all health plan M&As fail to create meaningful shareholder value. CFOs and management can take a stronger role in M&A deal evaluation, especially on revenue growth. 4 Use of Financial Statements for Valuation “I am considering to buy a small packing company. They offered me RMB 15 million and gave me their last 2 years’ Income Statements and Balance Sheets. I think it’s overpriced. How much do you think I should pay?” How will you use I/S and B/S to assess the target firm’s fair value? 5 Warren Buffet Emphasized importance of looking at a firm’s Competitive advantage of products Long-term growth potential… for good investment 6 Sound Fundamental Analysis One does not buy a stock, one buys a business. When buying a business, know the business. Good firms can be bad buys (if overpriced). Price is what you pay, value is what you get. Value of firm = Value of Debt + Value of Equity TA = L + SE (BV) on B/S 7 TA – L = SE SE (BV) vs. Market value of equity 8 Stock Price What is intrinsic value? Is the price overvalued? P/E=41: What earnings growth rate investors predicted? 9...
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...MULTIPLE REGRESSION After completing this chapter, you should be able to: understand model building using multiple regression analysis apply multiple regression analysis to business decision-making situations analyze and interpret the computer output for a multiple regression model test the significance of the independent variables in a multiple regression model use variable transformations to model nonlinear relationships recognize potential problems in multiple regression analysis and take the steps to correct the problems. incorporate qualitative variables into the regression model by using dummy variables. Multiple Regression Assumptions The errors are normally distributed The mean of the errors is zero Errors have a constant variance The model errors are independent Model Specification Decide what you want to do and select the dependent variable Determine the potential independent variables for your model Gather sample data (observations) for all variables The Correlation Matrix Correlation between the dependent variable and selected independent variables can be found using Excel: Tools / Data Analysis… / Correlation Can check for statistical significance of correlation with a t test Example A distributor of frozen desert pies wants to evaluate factors thought to influence demand Dependent variable: Pie sales (units per week) ...
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...Quartz heaters are rated according to the average power drawn from a 120 volt AC 60 Hz voltage source. Estimate the resistance (when operating) a 1200 watt quartz heater. NOTE: The voltage waveform for a 120 volt AC 60 Hz waveform is The factor of in the peak amplitude cancels when the average power is computed. One result is that the peak amplitude of the voltage from a 120 volt wall outlet is about 170 volts. Solution: Power watts ; where is average value of sinusoidal voltage, Average value of a sinusoidally oscillating signal is the peak value divided by Therefore Therefore 1 © ¥£ $ ¡ ! 3 © § ¥£¡ ¦QPIHG00F E¨¦¤¢ ¥£ & $ ¡ ! ¦%('%#" ¨¦¤¢ © § ¥£¡ (0 7 0 T § 02@ CA § @ 3 71 § 3 1 ¦D B29865)42§ § S@ § 0)R © (0)§ C D B(0 A § . 2 ANS:: CHAPTER 1. THE CIRCUIT ABSTRACTION Exercise 1.2 a) The battery on your car has a rating stated in ampere-hours which permits you to estimate the length of time a fully charged battery could deliver any particular current before discharge. Approximately how much energy is stored by a 50 ampere-hour 12 volt battery? b) Assuming 100% efficient energy conversion, how much water stored behind a 30 meter high hydroelectric dam would be required to charge the battery? Solution: a) b) Potential Energy b & a p) (S§ ¢3 ¦B) A & 3 ) V 9(B§ A C u ! s p& y x vus b & a wtc) ( § ) Electrical Energy; assume efficiency ...
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...Mao1, ,Xiao-Jun Zeng2 . : authors made equal contributions. arXiv:1010.3003v1 [cs.CE] 14 Oct 2010 Abstract—Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public’s response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in...
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...Data Mining D t Mi i Module 1 Introduction to Data Mining Dr. Jason T.L. Wang, Professor Department of Computer Science New Jersey Institute of Technology / Data Management: Its Evolution 1960s: – File management and network DBMS 1970s: – Relational DBMS 1980s: 980s – Non-first normal form, extended-relational, OO, deductive databases and application-oriented DBMS pp (spatial, scientific, CAD/CAM, etc.) 1990s - present: p – Data mining, digital library, and Web databases – Cloud databases, data science, and Big Data Data Mining © Jason Wang 2 Data Mining: Its Definition Data mining (knowledge discovery in databases): ) – Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Alternative names: – Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, analysis data archeology, data dredging archeology dredging, information harvesting, etc. Data Mining © Jason Wang 3 Data Mining: A Multidisciplinary Field Pattern Recognition Machine Learning Databases St ti ti Statistics Information Visualization Data Mining © Jason Wang 4 Data to be mined Text databases Web databases Scientific and biological databases Transactional databases Data Mining © Jason Wang 5 Knowledge to be discovered K l d t b di d Association (correlation) ...
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...This paper develops simple econometric models to analyze and forecast three components of the Bank of Canada commodity price index (BCPI), namely non-energy commodity prices (BCNE), the West Texas Intermediate crude oil price (WTI), and other energy prices. In the paper, we present different methodologies to identify transitory and permanent components of movements in these prices. A structural vector autoregressive (SVAR) model is used for real BCNE prices, a multiple structural-break technique is employed for real crude oil prices, and an errorcorrection model is constructed for real prices of other energy components. Then we use these transitory and permanent components to develop forecasting models. We assess our models’ performance in various aspects, and our main results indicate: (a) for real BCNE prices, most of the short-run variation is attributed to demand shocks, (b) the world economic activity and real U.S. dollar effective exchange rate explain much of the cyclical variation of real BCNE prices, (c) real crude oil prices have two structural breaks over the sample period, and their link with the world economic activity is strongest in the most recent regime, (d) real prices of other energy components are highly correlated with the U.S. economic activity, and they are co-integrated with real crude oil prices, (e) our models outperform benchmark models, namely a VAR model, autoregressive (AR) model and a random walk (RW) model, in terms of out-of-sample forecasting...
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...the bipolar junction transistor, which is a type of transistor. [pic] Figure 1: An example of a P-N Junction. Objectives The objectives of this experiment were to investigate the DC current-voltage characteristics of the PN junction diode, Experimentally verify theoretical model developed in lecture and extract ideality factor, and reverse saturation current. Produce a piece-wise linear model for the diode and Compare closed form, piece-wise linear model, and PSPICE simulations with experiment. A set of formulae was given in order to aid in the process of solving variables within the experiment. Some useful formulae in our experiment can be given the equations below. [pic] (1) [pic] (2) And the diode equation is: [pic] (3) Where Id given in Equation 1 is DC current through diode, and Vd given in Equation 2 is the voltage across the diode. Additionally: lo given in Equation 3 represents reverse saturation current, q stands for electron charge (1.6 x 10-19 C), k is the Boltzmann's constant (1.38 x 10-23 J/K), T stands for absolute temperature in Kelvin degree and finally n is the ideality...
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...2012 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0276146712463823 jmk.sagepub.com Blaine J. Branchik1 and Tilottama Ghosh Chowdhury1 Abstract This research chronicles the changes in the understudied and rapidly evolving male market segment using two related studies: (1) a content analysis of advertisements in fifty-one years of Sports Illustrated magazine and (2) an experiment involving age-based differences in consumer ad perceptions. Both investigate changing ad values and the ethnic diversity of ad models. Results indicate that the male market is becoming increasingly self-oriented in its values orientation as a result of broad societal changes and changing gender roles. Increasing use of black or African American models in key positions indicates a growing acceptance of minorities as representations of the ideal self among younger men, who express a preference for black or African American models. This finding speaks to the increasingly multicultural nature of society and the impact of minority celebrities on American culture. The results are indicative of the power of advertising in both reflecting and facilitating societal change. Keywords advertising, male market, societal change, ethnicity, gender, culture, macromarketing Introduction American men, as a cultural entity and market force, are undergoing rapid change. This metamorphosis is reflected in men’s increasing focus on their bodies (Alexander 2003; Pope, Phillips...
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