###### Point Estimate vs. Confidence Interval ...

Point Estimate A point estimate gives statisticians a single value as the estimate... **Read More**

The expected value of a random variable is the average of the possible outcomes of that variable, taking the probability weights into account. Therefore:

$$ E\left( X \right) =\sum _{ i=1 }^{ n }{ { X }_{ i }P\left( { X }_{ i } \right) } $$

An analyst anticipates the following returns from an asset:

$$ \begin{array}{c|c} {\textbf{Return}} & {\textbf{Probability}} \\ \hline {5\%} & { 65\%} \\ \hline {7\%} & { 25\%} \\ \hline {8\%} & { 10\%} \\ \end{array} $$

The expected value of the investment is *closest* to:

**Solution**

$$ \begin{align*} \text{Expected return} & = 0.05 × 0.65 + 0.07 × 0.25 + 0.10 × 0.08 \\ & = 0.0325 + 0.0175 + 0.008 \\ & = 0.058 \\ \end{align*} $$

The variance of a random variable is the sum of the squared deviations from the expected value weighted by respective probabilities. Therefore:

$$ { \sigma }^{ 2 }\left( X \right) =\sum _{ i=1 }^{ n }{ { \left[ { X }_{ i }-E\left( { X } \right) \right] }^{ 2 }P } \left( { X }_{ i } \right) =\left\{ { \left[ X-E\left( { X } \right) \right] }^{ 2 } \right\} $$

Using the data from the previous example, we can compute the variance of return:

$$ \begin{align*} { \sigma }^{ 2 }\left( X \right) & =0.65{ (0.05-0.058) }^{ 2 }+0.25{ (0.07-0.058) }^{ 2 }+0.10{ (0.08-0.058) }^{ 2 } \\ & = 0.000126 \\ \end{align*} $$

Variance is not easy to interpret because it has squared units. Therefore, we usually use the standard deviation, which has the same units as the expected value. To get the standard deviation, we use the square root of variance:

$$ \begin{align*} \text{Standard deviation} & = \sqrt{\text{Variance}} \\ &= \sqrt{0.000126} \\ & =0.01122 \text{ or } 1.12\% \\ \end{align*} $$

* Note:* You can always raise the variance to 0.5 power to get the same result.

$$ \begin{align*} \text{Standard deviation} & = \text{Variance}^{0.5} \\ &= 0.000126^{0.5} \\ & =0.01122 \text{ or } 1.12\% \\ \end{align*} $$

QuestionYou have been given the following data indicating the returns likely to be earned on a stock alongside the corresponding probabilities:

$$ \begin{array}{c|c} {\textbf{Return}} & {\textbf{Probability}} \\ \hline {4\%} & { 40\%} \\ \hline {5\%} & { 25\%} \\ \hline {6\%} & { 35\%} \\ \end{array} $$

The standard deviation of expected returns is

closestto:A. 0.00007475.

B. 0.0495.

C. 0.008646.

SolutionThe correct answer is

C.The first step involves determining the expected return:

$$ \begin{align*} E(X) & = (0.04 × 0.4) + (0.05 × 0.25) + (0.06 × 0.35) \\ & = 0.0495 \\ \end{align*} $$

Next, we must compute the variance of returns:

$$ \begin{align*} { \sigma }^{ 2 }\left( X \right) & =0.4(0.04–0.0495)^{ 2 }+0.25(0.05–0.0495)^{ 2 }+0.35(0.06 – 0.0495)^{ 2 } \\ & = 0.00007475 \\ \end{align*} $$

Lastly, we find the square root of variance to get the standard deviation of expected return:

$$ { \sigma }= \sqrt{0.00007475} = 0.008646 $$