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

See similar textbooks

Similar questions

Can you explain how I can find the variance for the data using the formula provided? I don’t understand what the numbers for E and X are.
Two variable are found to have a strong negative linear correlation. Pick the regression equation that best fits this scenario. y=0.82x−28 ˆy=0.32x−28 y= -0.82x+28 ˆy= -0.32x+28
A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a. For each 1-cm increase in height, weight tends to increase by about 0.32 kg b. There is no association between weight and height c. Height accounts for about 32% of the total variability in weight d. The correlation between weight and height is about 0.32
The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…
A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to the…
Consider the following variables: Y=daily productivity score (measured in points) X1=0 if undergraduate student,1 if graduate student X2=hours of sleep per night 1. The plot shown below could possibly be the graph of which model? 2. If you want to test whether type of student modifies the association between hours of sleep per night and daily productivity score, which model should you consider and what is the null hypothesis for this test? 3. Suppose you use Model 5 to describe the relationship between type of student, hours of sleep per night, and daily productivity score. You use the method of least squares to obtain: Y = -0.5 + 3 (X1) + 1.5 (X2) + 2.5 (X1)(X2).
Select the most appropriate response. If the correlation between a person’s age and annual income is 0.60, then the coefficient of determination tells us that: 36% of the variation in a person’s annual income can be explained by the predictor variable age. 36% of a person’s annual income can be explained by their age 60% of the variation in a person’s annual income can be explained by the predictor variable age 60% of a person’s annual income can be explained by their age
A pediatrician wants to determine the relation that exists between a child's height (x) and head circumference (y). She randomly selects 11 children from her practice and measures their height and head circumference in inches. She finds that the correlation is 0.694, and the regression equation is y = 0.294x + 2.02. What proportion of the variation in head circumference can be explained by the variation in the values of height? Round your answer to three decimal places. %
The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.972. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y= - 0.0070x + 44.4405. Complete parts (a) and (b) below. Click the icon to view the data table. ..... (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. Data Table (Round to one decimal p Full data set gas mileage Miles per Weight (pounds), x Weight (pounds), x Miles per Gallon, y Car Car Gallon, y…
A researcher wants to determine if there is a relationship between the number of hours spent by students on social media sites and self-esteem levels. A random sample of 10 statistics students was part of the study. Use manual calculations at α = 0.05 to calculate whether there is a significant relationship between the two variables. Show all your calculations.
Suppose you are interested in seeing whether the total number of days students are absent from high school correlates with their grades. You obtain school records that list the total absences and average grades (on a percentage scale) for 80 graduating seniors. You decide to use the computational formula to calculate the Pearson correlation between the total number of absences and average grades. To do so, you call the total number of absences X and the average grades Y. Then, you add up your data values (ZX and EY), add up the squares of your data values (EX² and ΣY²), and add up the products of your data values (ΣXY). The following table summarizes your results: ΣΧ 380 ΣΥ 5,820 ⠀ ΣΧ2 2,708 ΣΥ2 440,838 ΣΧΥ 26,709 The sum of squares for the total number of absences is SSx = The sum of squares for average grades is SSy = The sum of products for the total number of absences and average grades is SP = The Pearson correlation coefficient is r = Suppose you want to predict average grades…
Two variable are found to have a strong positive linear correlation. Pick the regression equation that best fits this scenario. y = 0.85x + 23 ŷ = 0.35x + 23 0.35x 23 | y = 0.85x – 23

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS