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Contact [7]
4 years ago
8

A broker has calculated the expected values of two different financial instruments X and Y. Suppose that E(x)= $100, E(y)=$90 SD

(x)= 90$ and SD(y)=$8. Find each of the following.
a. E(X+ 10) and SD(X+ 10)
b. E(5Y) and SD(5Y)
c) E(X+ Y) and SD(X+ Y)
d) What assumption must you make in part c?
Mathematics
1 answer:
Sveta_85 [38]4 years ago
8 0

Expectation is linear, meaning

E(<em>a X</em> + <em>b Y</em>) = E(<em>a X</em>) + E(<em>b Y</em>)

= <em>a </em>E(<em>X</em>) + <em>b</em> E(<em>Y</em>)

If <em>X</em> = 1 and <em>Y</em> = 0, we see that the expectation of a constant, E(<em>a</em>), is equal to the constant, <em>a</em>.

Use this property to compute the expectations:

E(<em>X</em> + 10) = E(<em>X</em>) + E(10) = $110

E(5<em>Y</em>) = 5 E(<em>Y</em>) = $450

E(<em>X</em> + <em>Y</em>) = E(<em>X</em>) + E(<em>Y</em>) = $190

Variance has a similar property:

V(<em>a X</em> + <em>b Y</em>) = V(<em>a X</em>) + V(<em>b Y</em>) + Cov(<em>X</em>, <em>Y</em>)

= <em>a</em>^2<em> </em>V(<em>X</em>) + <em>b</em>^2 V(<em>Y</em>) + Cov(<em>X</em>, <em>Y</em>)

where "Cov" denotes covariance, defined by

E[(<em>X</em> - E(<em>X</em>))(<em>Y</em> - E(<em>Y</em>))] = E(<em>X Y</em>) - E(<em>X</em>) E(<em>Y</em>)

Without knowing the expectation of <em>X Y</em>, we can't determine the covariance and thus variance of the expression <em>a X</em> + <em>b Y</em>.

However, if <em>X</em> and <em>Y</em> are independent, then E(<em>X Y</em>) = E(<em>X</em>) E(<em>Y</em>), which makes the covariance vanish, so that

V(<em>a X</em> + <em>b Y</em>) = <em>a</em>^2<em> </em>V(<em>X</em>) + <em>b</em>^2 V(<em>Y</em>)

and this is the assumption we have to make to find the standard deviations (which is the square root of the variance).

Also, variance is defined as

V(<em>X</em>) = E[(<em>X</em> - E(<em>X</em>))^2] = E(<em>X</em>^2) - E(<em>X</em>)^2

and it follows from this that, if <em>X</em> is a constant, say <em>a</em>, then

V(<em>a</em>) = E(<em>a</em>^2) - E(<em>a</em>)^2 = <em>a</em>^2 - <em>a</em>^2 = 0

Use this property, and the assumption of independence, to compute the variances, and hence the standard deviations:

V(<em>X</em> + 10) = V(<em>X</em>)  ==>  SD(<em>X</em> + 10) = SD(<em>X</em>) = $90

V(5<em>Y</em>) = 5^2 V(<em>Y</em>) = 25 V(<em>Y</em>)  ==>  SD(5<em>Y</em>) = 5 SD(<em>Y</em>) = $40

V(<em>X</em> + <em>Y</em>) = V(<em>X</em>) + V(<em>Y</em>)  ==>  SD(<em>X</em> + <em>Y</em>) = √[SD(<em>X</em>)^2 + SD(<em>Y</em>)^2] = √8164 ≈ $90.35

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