b) σ²w =0.65^2*6.58^2+0.35^2*18.55^2+2*0.65*0.35*6.58*18.55*0.425= 84.0484
σw = √84.048 = 9.1678
c) σ²w =0.35^2*6.58^2+0.65^2*18.55^2+2*0.65*0.35*6.58*18.55*0.425= 174.2903
σw = √174.2903 = 13.2019
d) higher expected return = part c)
greater risk =part c)
investment of 35% in bonds and 65% in real estate
Previously, you studied linear combinations of independent random variables. What happens if the variables are not...
Previously, you studied linear combinations of independent random variables. What happens if the variables are not independent? A lot of mathematics can be used to prove the following: Let x and y be random variables with means μx and μy, variances σ2x and σ2y, and population correlation coefficient ρ (the Greek letter rho). Let a and b be any constants and let w = ax + by for the following formula. μw = aμx + bμy σ2w = a2σ2x +...
Let Xi, x,, ,X, be independent random variables with mean and variance σ . Let Y1-Y2, , Y, be independent random variables with mhean μ and variance a) Compute the expected value of W b) For what value of a is the variance of W a minimum? σ: Let W-aX + (1-a) Y, where 0 < a < 1. Let Xi, x,, ,X, be independent random variables with mean and variance σ . Let Y1-Y2, , Y, be independent random...
You are given three independent random variables: X, Y, and Z. The expected values of each are 0, and the variances of each are 1. Let U1 =Y + Z and let U2 = X – Y. (a) What are the variances of U1 and U2? (b) What is Cov(U1, U2)? (c) Combining your answers to (a) and (b), what is the correlation coefficient p between U1 and U2?
Let X and Y be independent Gaussian(0,1) random variables. Define the random variables R and Θ, by R2=X2+Y2,Θ = tan−1(Y/X).You can think of X and Y as the real and the imaginary part of a signal. Similarly, R2 is its power, Θ is the phase, and R is the magnitude of that signal. (a) Find the joint probability density function of R and Θ, i.e.,fR,Θ(r,θ).
Let X and Y be two independent random variables with X =d R(0, 2) and Y =d exp(1). (a) Use the convolution formula to calculate the probability density function of W =X+Y. (b) Derive the probability density function of U = XY .
3. In this question, you will identify the distribution of the sum of independent random variables. I expect you will find that the mgf approach is your friend. (a) Let X and Y be independent Poisson random variables with means A1 and 12, respectively, and let S = X+Y. What is the distribution of S? (b) Let X and Y be independent normal random variables with means Husky and variances 07. 07. respectively, and let S = X+Y. What is...
(a) Let X and Y be independent random variables both with the same mean u +0. Define a new random variable W = ax + by, where a and b are constants. (i) Obtain an expression for E(W). (ii) What constraint is there on the values of a and b so that W is an unbiased estimator of u? Hence write all unbiased versions of W as a formula involving a, X and Y only (and not b). [2]
(#20) Let You X, Yz be independent random Variables with Ely:)= “; V(%:)= é, i=1,2,3. Let W, = 2X –X3, W2 = 4%, + x3 + x3, W3 = X, +5X2. Ca) let W=M). Find o ?{w} the covariance matrix of W using matrices, (6) Find a matrix B such that Y=BW,
Let Y, Y2, Yz and Y4 be independent, identically distributed random variables from a population with mean u and variance o. Let Y = -(Y, + Y2 + Y3 +Y4) denote the average of these four random variables. i. What are the expected value and variance of 7 in terms of u and o? ii. Now consider a different estimator of u: W = y + y + y +Y4 This an example of weighted average of the Y. Show...
Exercise 6.15. Let Z, W be independent standard normal random variables and-1 < ρ < l. Check that if X-Z and Y-p2+ VI-p-W then the pair (X, Y) has standard bivariate normal distribution with parameter ρ. Hint. You can use Fact 6.41 or arrange the calculation so that a change of variable in the inner integral of a double integral leads to the right density function.