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03-08-2012, 19:59   #1
markg86
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Could anyone help with this financial economics problem? be really appreciated

intro to financial economics--nuig exam paper

You are considering investing in two shares, X and Y. The following data are available for the two shares:
Share X Share Y
Expected Return 11% 9%
Standard Deviation 16% 12%
Beta 1.30 0.95

What happens to the expected return and standard deviation of returns of the portfolio if the following conditions exist?
i) The correlation of returns between Share X and Y is +1.0.
ii) The correlation of returns between Share X and Y is +0.3
iii) The correlation of returns between Share X and Y is -0.8
What conclusions can you draw about the importance of the correlation coefficient?

I know the correlation explains how the share move together but I cant find anything about beta in my notes. I know its a measure of volatility but don't now how to use it here. emailed my lecturer but his off on holidays got automatic response thingy

Il sent great karma for any reply s.

Cheers

Last edited by markg86; 03-08-2012 at 20:03.
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03-08-2012, 23:23   #2
zaraba
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Beta is the correlation between the share and the index which it is benchmarked against.
Its from the Capital Asset Pricing model.

A beta of 1.5 means that a stock will 1.5 times greater then the benchmark.

http://en.wikipedia.org/wiki/Beta_%28finance%29
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04-08-2012, 15:44   #3
highly1111
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Beta is basically by how much it beats the market return. Alpha is market return and beta is anything above that.
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04-08-2012, 20:28   #4
markg86
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I have the formulas for portfolio variance and the standard deviation is the sqrt of the variance. I also have a formula for portfolio expected return and i can figure out the covariance from the correlation givin.

however I usually would input a weight assigned to each share given in the question.

so here im givin beta instead.

anyone know how I can use it??

Portfolio variance = (weight(1)^2*variance(1) + weight(2)^2*variance(2) + 2*weight(1)*weight(2)*covariance(1,2)

Return of portfolio = (W1 x R1) + (W2 x R2)
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