An option to buy a stock is priced at $150. If the stock closes above 30 next Thursday, the option will be worth $1000. If it cl
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Step-by-step explanation:
An option to buy a stock is priced at $150. If the stock closes above 30 next Thursday, the option will be worth $1000. If it closes below 20, the option will be worth nothing, and if it closes between 20 and 30, the option will be worth $200. A trader thinks there is a 50% chance that the stock will close in the 20-30 range, a 20% chance that it will close above 30, and a 30% chance that it will fall below 20.
a) Let X represent the price of the option
<h3><u> x P(X=x) </u></h3>
$1000 20/100 = 0.2
$200 50/100 = 0.5
$0 30/100 = 0.3
b) Expected option price
Therefore expected gain = $300 - $150 = $150
c) The trader should buy the stock. Since there is an positive expected gain($150) in trading that stock option.
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Answer:
Step-by-step explanation:
Hello!
Given the variables:
X₁: Median Age
X₂: Median Income
b) Considering it from a logical point of view, income changes with age, for example, the more experienced the worker is you would think he would get a better paying job than a younger worker who is just starting. Then the dependent variable will be the median income and the independent variable will be the median age.
a) and c)
To see if there is a linear regression between the median income and median age you have to conduct a hypothesis test for the slope. If the slope is equal to zero, there is no linear regression between the two variables, if it is different to zero, there is a regression between the two of them:
H₀: β=0
H₁: β≠0
α: 0.05
The estimated regression equation for this regression ^Yi= 20.01 + 0.50X
The standard deviation for the slope is Sb= 0.11
And the p-value for the test is 0.0022
The p-value is less than the level of significance I choose, so the decision is to reject the null hypothesis. You can conclude that there is a linear regression between these two variables.
The correlation coefficient between the median income and the median age is r= 0.87 ⇒ This means you could expect a positive and strong linear correlation between the two variables.
d)
The slope represents how much the variable Y is modified whenever the variable X increases one unit.
In this example: Is the modification of the population mean of the median income, when the median age increases one year.
