LG 5 P8-18 Graphical derivation of beta A firm wishes to estimate graphically the betas for twoassets, A and B. It has gathered the return data shown in the following table for themarket portfolio and for both assets over the last 10 years, 2006-2015.Year2006200720082009201020112012201320142015Market portfolio2-13-4-8161015Actual return813Asset A11%8-4301914181217Asset B16%11-103-33022291926a. On a set of "market return (x axis)-asset return (y axis)" axes, use the data givento draw the characteristic line for asset A and for asset B.b. Use the characteristic lines from part a to estimate the betas for assets A and B.c. Use the betas found in part b to comment on the relative risks of assets A and B.

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Answered: LG 5 P8-18 Graphical derivation of beta…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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O 599.1 O 616.6 O 602.7 596.4
Based on the Regression below, what is the relation between the GDP and gross fixed capital formation(GFCF), trade openness(trade openness), foreign direct investment (FDI). Explain by using A multiple linear regression model (Example y = b0 + b1x1 + b2x2 + …+ bkxk )
World supply of recycled silver, Q, in millions of ounces and the price of silver, P, in dollars per ounce, each year are given in the table below. Year 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Q 232.9 216.0 192.7 174.9 166.5 164.4 167.7 167.7 169.9 169.4 P. 35.12 31.15 23.79 19.08 15.68 17.14 17.05 15.71 16.21 15.7O Which of the following figures show possible supply curves for the the data? Select all possible correct choices. O P (milion $/oz) 35 H 25 15 5 + O (million ozs) 240 160 180 200 220 O P (milion $/oz) 35 25 15 - O (million ozs) 240 160 180 200 220 O P (milion $/oz) 35 F 25 15 + O (million ozs) 240 160 180 200 220 O P (milion $/oz) 35 H 25 15 Q (million ozs) 240 160 180 200 220
Question Three For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X)|Sales (Y) 2001 44 2002 40 2003 42 2004 46 2005 48 2006 52 2007 54 2008 58 2009 56 2010 60 10 9 11 12 11 12 13 13 14 15 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. a) Specify the estimation model you will use (the econometric form). b) Identify the independent and the dependent variables. c) Use an econometric package of your choice (EViews, SPSS, STATA etc) to estimate the model. d) From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table).
China GDP Year ($ trillions) 2005 2.3 2008 4.6 2011 7.5 2014 10.5 2017 12.0 2020 14.5 2023 17.5 (Source Bloomberg.com U.S. GDP ($ trillions) 13.1 + 14.7 15.5 2032 2035 17.4 19.4 20.6 21.8 China GDP U.S. GDP ($ trillions) 23.2 24.6 26.1 27.7 29.4 30.6 Year ($ trillions) 2026 21.2 2029 25.6 30.9 37.3 2038 45.1 2040 51.1 2. China's GDP vs. United States' GDP I. It is widely predicted that China's economy, mea- sured by Gross Domestic Product (GDP), will even- tually overtake that of the United States. Table 3.17 shows the GDPs for China and the United States from 2005 and projected to 2040 under the assump- tion that China's GDP will grow at an average annual rate of 6.5% and that the United States' GDP will grow at an average annual rate of 2%. Investigate when China's GDP will reach that of the United States under these assumptions about the countries" growth rates by using the following steps. 1. a. Create a scatter plot for the data, with x equal to the number of years after 2000 and…
TIP ($) 250 225 200 175 150 125 100 75 50 25 0 0 A SONNY'S SCATTERPLOT B 100 200 300 400 Ⓒ2020 Radha Bose Florida State University Department of Statistics C D E SALE AMOUNT ($) *F H 500 600 700 800 900 1000 1100 1200 1. Which sale received the best tip, that is, which sale had the tip that was highest above the tip predicted for it? 2. Which data point has the smallest residual? 3. The residual for data point B is closest to
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Fifty states Here is a data set on various measures of the50 United States. The Murder rate is per 100,000, HSGraduation rate is in %, Income is per capita income indollars, Illiteracy rate is per 1000, and Life Expectancy isin years. Find a regression model for Life Expectancywith three predictor variables by trying all four of thepossible models.a) Which model appears to do the best?b) Would you leave all three predictors in this model?c) Does this model mean that by changing the levels ofthe predictors in this equation, we could affect lifeexpectancy in that state? Explain. d) Be sure to check the conditions for multiple regres-sion. What do you conclude?
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Day Highest Temperature Lowest Temperature Thursday 07/07 24 Degree 14 Degree Friday 08/07 27 Degree 15 Degree Saturday 09/07 25 Degree 15 Degree Sunday 10/07 27 Degree 16 Degree Monday 11/07 29 Degree 16 Degree Tuesday 12/07 31 Degree 17 Degree Wednesday 13/07 29 Degree 15 Degree Thursday 14/07 28 Degree 16 Degree Friday 15/07 28 Degree 17 Degree Saturday 16/07 29 Degree 18 Degree For the data above , use the linear forecasting model which is y = mx + c to calculate and discuss the followings: Show the steps of calculation of m value and discuss the answer. Show the steps of calculation of c value and discuss the answer. Using the calculated 'm' and 'c' values, forecast the temperature for day 11 and day 14. Hint - Use the following formulas to calculate the ‘m’ and ‘c’ values. m = c =
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