1. Choosing an Investment Portfolio
P.V. Viswanath
Based on Bodie and Merton

2. Returns and Return Uncertainty
If an investor buys an asset, a, at time 0 for $P0, receives a cashflow at time 1 of $D1 and sells it at time 1, for $P1, the return on the asset is defined as Ra,1 = (P1 + D1 – P0)/P0
At time 0, the actual value of Ra,1 is unknown.
Suppose there are three different possible outcomes, corresponding to three different states: states 1, 2 and 3.
Thus Ra,1 could be equal to 3%, 7% or 10% with probabilities of 0.3, 0.2 and 0.5 for the three different states.
The mean or expected value of Ra,1 is computed as 0.3(3%) + 0.2(7%) + 0.5(10%) = 7.3%
The expected value is a kind of average outcome, where the different possible outcomes are weighted by the probabilities.
E(Ra) is the way we represent the expected return on asset a.
P.V. Viswanath

3. Return Volatility
Consider another asset, b, which has returns of 1%, 7% and 11.2% in the three different states.
The expected return of asset b is 0.3(1%) + 0.2(7%) + 0.5(11.20%), which is also 7.3%.
However, asset b’s returns are said to be more volatile than those of asset a, meaning roughly that the range of possible outcomes is greater, or that more extreme outcomes – both positive and negative – are more likely.
P.V. Viswanath

4. Variance
The variance of returns is computed as the mean squared deviation from the expected return.
The squared deviation is a measure of how extreme a return is.
Taking the mean computes the average value of these deviations.
The square root of the variance is called the standard deviation or volatility.
Variance is measured in percent-squared.
Standard deviation is measured in the same units as returns, viz. percent.
P.V. Viswanath

5. Return volatilities of Assets a & b
Probabilities
Return (Asset a)
Deviation from Mean Return
Squared Deviation
0.3 3
-4.3
18.49
0.2 7
-0.3
0.09
0.5 10
2.7
7.29
Probabilities
Return (Asset b)
Deviation from Mean Return
Squared Deviation
0.3 1
-6.3
39.69
0.2 7
-0.3
0.09
0.5
11.2
3.9
19.53
P.V. Viswanath

6. Variance and Standard Deviations
We see that the variance of returns for asset a is 9.21, computed as 0.3(18.49) + 0.2(0.09) + 0.5(7.29) and the standard deviation is 3.035%
The variance of returns for asset b can be similarly computed and is 19.53; the standard deviation is 4.419%.
sa is used to represent the standard deviation of returns on asset a, while Var(Ra) or sa2 is used to represent the variance of returns on asset a.
We see that, as expected asset b, which has more extreme outcomes, also has a higher volatility.
P.V. Viswanath

7. Portfolios
A portfolio is defined as a set of assets and the proportions of money invested in those assets.
Thus, if we agree on a set of assets, i = 1,…,n, then a set of weights wi, i=1,..,n represents a portfolio.
If the assets are all stocks – SBUX, VZ and F, then (0.1, 0.45, 0.45) is a portfolio representing an investment of $100 in SBUX, $450 in VZ and $450 in F; or equivalently, $50 in SBUX, $225 in VZ and $225 in F.
Even though the dollar returns will vary for the two portfolios, the percentage returns will always be the same; hence the mean return and variance of returns will be the same.
P.V. Viswanath

8. Portfolio Return
Suppose we construct a portfolio, consisting of $30 in asset a and $70 in asset b; i.e. wa = 0.3 and wb = 0.7.
Then the return on the portfolio in any given state will be waRa + wbRb.
We can then compute the expected return for this portfolio, as well as its variance of returns.
A riskfree asset always has the same return independent of the state.
Hence, its standard deviation of returns is zero.
P.V. Viswanath

9. Combining risky and riskless assets
Suppose we combine a risky portfolio, P with a risk-free asset, f.
Let C denote the complete portfolio, and y the proportion invested in the risky portfolio. Then,
rC = y rP + (1-y) rf
E(rC) = y E(rP) + (1-y) rf, and
sC = y sP
Suppose E(rP) = 15%, rf = 7%, and sP = 22%, then we can compute the trade-off between expected return and volatility obtained by putting different proportions in the risky portfolio and the risk-free asset.
P.V. Viswanath

10. The Risk-Reward Trade-Off Line
P.V. Viswanath

11. Covariance and Correlation
If we have two assets a and b, the covariance between the returns on the two assets is defined as the weighted average of the product of deviations of each return from its mean return.
The correlation is the ratio of the covariance to the product of the two standard deviations. The correlation can only take values between -1 and +1.
We can use the deviations of the returns on assets a and b in our earlier example to compute the covariance coefficient and the correlation coefficient.
P.V. Viswanath

12. Covariance
Probabilities
Ra – E(Ra)
Rb – E(Rb)
Product of deviations
0.3
-4.3
-6.3
27.09
0.2
-0.3
0.09
0.5
2.7
3.9
10.53
The covariance can be computed as 0.3(17.49) + 0.2(0.49) + 0.5(19.24) = 13.41
The correlation coefficient is /(3.035 x 4.419) =
P.V. Viswanath

13. Covariance
The covariance (and correlation coefficient) is a measure of how closely two variables move together.
In our case, both assets have high returns in state 1 and low returns in state 2; hence they have a high correlation. If there were another asset, c, with low returns in state 1 and high returns in state 2, it would have a negative correlation with asset a.
An asset, d, with a return of 1%, 7% and 45% in the three states can be shown to have a correlation coefficient of only 0.93 with asset a.
This correlation is less than the correlation between assets a and b because asset d behaves quite differently from asset a in state 3, even though state 3 is a good state for both assets.
If we have many different states, we can construct assets with different kinds of correlation coefficients between their returns, corresponding to reality.
P.V. Viswanath

14. Combinations of risky assets
Suppose we invest positive amounts in two risky assets that are imperfectly positively correlated.
Then, because of the potential for diversification or offset between the returns of different assets in a portfolio, the standard deviation of returns on a portfolio is less than the weighted average of the standard deviation of returns on the assets making up the portfolio.
The smaller the correlation coefficient, the greater the possibility of diversification and vice-versa.
Thus, if we combine assets a and b in a portfolio, there will not be much diversification; however, if we combine assets a and d, there will be more diversification, while there will be even more diversification and potential for variance reduction if we combine assets a and c.
P.V. Viswanath

15. Combinations of risky assets
Suppose we combine two risky assets, D and E, with portfolio weights wD and wE.
Then the expected return and the variance of returns on the portfolio are given by
P.V. Viswanath

16. Combinations of risky assets
P.V. Viswanath

17. Optimal Combinations of risky assets and the risk-free asset
If we have assets D and E, as above, but we have, in addition, a risk-free asset as well.
Then, we can think of the optimal combination of the three assets as being achieved in two steps.
First compute the optimal combination, P, of assets D and E. This portfolio is also called the tangency portfolio.
Next combine this portfolio with the risk-free asset.
In practice, the optimal risky portfolio will depend upon the risk-free rate, as well, as can be seen from the graph in the next slide.
P.V. Viswanath

18. Optimal Combinations of risky assets and the risk-free asset
P.V. Viswanath

19. The Tangency Portfolio
The proportions of assets D and E in the tangency portfolio are given by the formulas below:
The particular proportions that the investor would then invest in the tangency portfolio versus the risk-free asset depends on his/her risk aversion. The greater the risk aversion, the more will be invested in the risk-free asset.
P.V. Viswanath

20. The minimum variance portfolio
The proportions of assets D and E in the minimum variance portfolio are given by the formulas below:
Of course, no rational investor would actually hold this portfolio, unless s/he were extremely risk-averse.
P.V. Viswanath

21. Efficient Frontier with Two Assets
The efficient frontier is the set of portfolios of risky assets offering the highest possible expected rate of return for any given standard deviation.
With two risky assets and one risk-free asset, the efficient frontier is precisely the line starting from the risk-free asset that is tangential to the combination line consisting of combinations of the two risky assets.
For any standard deviation of returns the highest return is given by some portfolio on the above straight line.
P.V. Viswanath

22. Efficient Frontier with many assets
Suppose there are many risky assets, but no risk-free asset.
Then conceptually, we can imagine taking pairs of assets and drawing their combination lines. Any point on one of these combination line is a portfolio of those two particular assets. We could then imagine combination lines where the two risky investments, themselves, are from the “combination” portfolios generated above.
There would be a very large, potentially infinite number of these combination lines.
If we consider all of the points on these infinite combination lines, we would have a dense area, bounded on the left by a parabola.
P.V. Viswanath

23. Efficient Frontier with Many Risky Assets
P.V. Viswanath

24. Efficient Frontier with Many Risky Assets and a Risk-free Asset
In this case, the optimal combination of risky assets is once again found as the point of tangency between a straight line from the point representing the riskless asset and the efficient frontier of risky assets.
The straight line connecting the riskless asset and the tangency point representing the optimal combination of risky assets is the best risk-reward trade-off line available – that is, the efficient frontier.
P.V. Viswanath

25. Efficient Frontier with Many Risky Assets and a Risk-free Asset
P.V. Viswanath