Problem 1 (We consider an investment problem with 3 risky assets. You are given thatThe expected return rate of these three risky assets are ₁ = 0.08, 2₂ = 0.13 andT3 = 0.16 repsectively.The variances of return rate of three risky assets are o2 = 0.02, 0² = 0.05 and03 = 0.1 respectively.●●●We assume that the returns of the risky assets are mutually uncorrelated (i.e.cov(ri,ri) = 0 for all i ‡ j.(a) Find the minimum variance portfolio with expected return Up = 0.1 usingLagrange method. Is the portfolio efficient? Explain your answer.(*Note: You also need to demonstrate how do you solve the relevant equationsin your solution.)(b) (True/False) Suppose that the investor is seeking for a portfolio which canachieve (1) smallest variance of portfolio return and (2) achieve expected returnnot less than μp (i.e. Tp Hp), then the investor conjectures that the optimalportfolio must be the minimum variance portfolio with expected return μp. Giveyour comment on the correctness of investor's conjecture.(c) Suppose that the investor is seeking for a portfolio which the variance ofportfolio cannot exceed omax = 0.5, find the portfolio with maximum expectedreturn. Provide justification to your solution.(Hint: For (b), you can get the answer using any method.)

Answered: Problem 1 ( We consider an investment…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 5SE: What does the y -intercept on the graph of a logistic equation correspond to for a population...

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Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
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Suppose that index model for Stocks A and B is estimated from excess returns with the following results : Ra 0.04 +0.6Rm+ea , Rb = - 0.04 + 1.3Rm + eb Risk on the market is 30% , R-squared of A is 30%R - squared of B is 40% , security A residual variance is
2
It can be conjectured that the annual return a security and the market return are related by the following regression model y=mx+b+ε Where y is the annual return of the security, x is the annual return of the market, b is the intercept, Ɛ is the normally distributed noise, and Return = value at end of the year + received dividends during the year - value at the beginning of the year. Test this model by retrieving annual data on a security of your choice. Choose a financial index such as S&P 500 as the indicator of the market, and retrieve the data. Use the most recent 20 years as the time span of the data. Perform regression analysis and make sure to include the hypothesis in your study. Provide your results and write your conclusions. Include all relevant information and conclusions, significances, the final regression model, coefficient of determination, graph of the regression line accompanied in the scatterplot, extent of residuals, and normality of residuals. Does the model…
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Suppose that in a certain chemical process the reaction time y (hr) is related to the temperature (°F) in the chamber in which the reaction takes place according to the simple linear regression model with equation y = 5.10 0.01x and a = 0.085. USE SALT (a) What is the expected change in reaction time for a 1°F increase in temperature? For a 9°F increase in temperature? 1°F increase hr hr 9°F increase (b) What is the expected reaction time when temperature is 220°F? When temperature is 260°F? 220°F hr ✓hr 260°F 2.5 (c) Suppose five observations are made independently on reaction time, each one for a temperature of 260°F. What is the probability that all five times are between 2.37 and 2.63 hours? (Round your answer to four decimal places.) (d) What is the probability that two independently observed reaction times for temperatures 1° apart are such that the time at the higher temperature exceeds the time at the lower temperature? (Round your answer to four decimal places.) 0.4602 X You…
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If the equation of the regression line that relates hours per week spent in the tutor lab, x, to GPA, y, is y=2.1+0.28, then the best presdiction for the GPA of students who never go into the lab would be 2.1 True Or False
45. Table 1.2 shows the mean annual compensation of construction workers. bab Abor Cesaione TABLE 1.2 Construction Workers' Average Annual Compensation Annual Total Compensation (dollars) Year 1999 42,598 2000 44,764 2001 47,822 2002 48,966 Source: U.S. Bureau of the Census, Statistical Abstract of the United States, 2004-2005. (a) Find the linear regression equation for the data. (b) Find the slope of the regression line. What does the slope represent? (c) Superimpose the graph of the linear regression equation on a scatter plot of the data. (d) Use the regression equation to predict the construction workers' average annual compensation in the year 2008. Tabl

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