Suppose you just bought one share of stock A and want to hedge it by shorting stock B. How many shares of B should you shortto minimize the variance of the hedged position?Assume that the variance of stock A's return is o?; the variance of B's return is o?; their correlation coefficient is p.Please show your derivation by attaching a file below.Hint:1. we don't restrict the solution to be an integer, it can be a non-negative value2. we don't set constraints for the target return

Correlation Coefficient The correlation may be a statistical measure of the strength of the link…

Answered: Suppose you just bought one share of…

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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Q3
Suppose that you are modelling cooling costs (y) as a function of the daily high temperature (x) for a building, using a simple linear regression model. You have data consisting of pairs of observations (x, y) over many years. Suppose that from this data you find a Pearson correlation between x and y of 0.5. If tomorrow the temperature is 3 standard deviations above the mean, how many standard deviations above the mean would you expect the cooling costs to be? If it is true that, tomorrow, the temperature is as given in the previous problem, then the expectation that you gave is the exact cooling cost that will occur. True O False
The correlation coefficient between the yearly returns of two mutual funds is 0.20. What does that mean about the strength and direction of the linear relationship between the returns of the two funds?
Answer the following: 2. Describe the form, direction, and strength of the relationship between these variables. Does there appear to be any outliers? 3. The equation of least-squares regression line is y = 6.871 + 0.061x. This line goes through the points (0, 6.9) and (700, 49.5). Use these points to add the regression line to your scatterplot above. 4. One animal is farthest from the regression line and is an outlier for these two variables. Find the name of this animal and write a sentence describing how its position differs from the overall relationship among the other animals. 5. The elephant has the longest gestation period of these animals. a. Considering only the gestation variable, would you say that the elephant is an outlier? Write a sentence or two explaining why or why not? b. Considering both variables and the relationship between average life span and average gestation period, would you say the elephant is an outlier now? Write a sentence or two explaining why or why…
4:42 ul LTE AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-square. The well-known psychologist Dr. Elbod has established what he calls his Generalized Anxiety Scale (GAS). The GAS, which is a scale from o to 10, measures the "general anxiety" of an individual, with higher GAS scores corresponding to more anxiety. (Dr. Elbod's assessment of anxiety is based on a variety of measurements, both physiological and psychological.) We're interested in making predictions about individuals' sleep behavior based on their GAS scores. The bivariate data below give the GAS score (denoted by x) and the number of hours of sleep last night (denoted by y ) for each of the fifteen adults in a study. The least-squares regression line for these data has equation =8.36-0.25x. This line, along with a scatter plot of the sample data, is shown in Figure 1. Sleep time, y (in hours) GAS score, x 6.0 8.1 6.5 5.9 10- 5.1 6.0 7.9 5.7 7.1 6.7 1.0 7.4 8.1 7.2 3.0 6.9 2.1 8.1 9.0 5.8 9.2…
You conducted a regression analysis between the number of absences and number of tasks missed by your 5 classmates in Statistics and Probability. It resulted that the regression line is y = 0.65x + 1.18. What is the predicted number of tasks missed of a learner who is always present? a. The learner has 1 task missed. b. The learner has less than 2 tasks missed. c. The learner has more than 2 tasks missed. d. The learner has no task missed.
Can you solve this using the table at the second photo?
From the table below, solve the following. b. Find mean of x [Select] X a. Find the Pearson correlation coefficient [Select] c. Find the mean of y [Select] 3 7 8 10 6 5 d. Find the regression equation [Select] [Select] y = 1.05 +0.92x y = 3.74 +0.68x y = 4.08 +0.81x y = 5.13 +0.73x y 4 11 12 7 5 3
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 0.5 1 1.5 2 3 3.5 4.5 Midterm Grades 63 66 68 72 74 93 94 Table Step 4 of 6 : Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.
A researcher wants to include the variable "skill" in a regression, but cannot find a proper metric to measure it, so they just decide not to include it in the model. You know how to measure skill and run the regression with it included and find that some of the original coefficients change. What would explain this? Oa. The covariance between skill and each of the other regressors is zero. O b. The B for skill is zero. Oc. The covariance between skill and each of the other regressors is not zero. O d. The variance of skill is increasing. What are the forecasts for years 7,-8 and 9 using the following data and a second-order autoregressive model where = -3.06, $1 = 0.86 and $2 = 0.94. Year Data 4. 6. 8. 13 16 19 3. 4. O a. =11.980 8=14.800 9 = 23.561 Ob. 7= 28.320 8 39.155 g 57.234 O c. 7=31.140 f8 = 47.916 Models in which all the error terms have the same variance are said to exhibit... O a. Multicollinearity. O b. Homoscedasticity O C. Heteroscedasticity. O d. Autocorrelation. Clear…
In regression, the larger (stronger) the correlation between Y and X, the 1. smaller the standard error of estimate and the greater the accuracy of prediction. 2.greater the standard error of estimate and the smaller the accuracy of prediction 3.greater the standard error of estimate and the greater the accuracy of prediction 4.smaller the standard error of estimate and the smaller the accuracy of prediction.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 5 5.5 Midterm Grades 63 67 76 78 84 85 90 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer to three decimal places.

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