Chapter 11- The CAPM.ppt
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1、Chapter 11: The CAPM,Corporate Finance Ross, Westerfield, and Jaffe,Outline,Portfolio theory The CAPM,Expected return with ex ante probabilities,Investing usually needs to deal with uncertain outcomes. That is, the unrealized return can take on any one of a finite number of specific values, say r1,
2、r2, , rS. This randomness can be described in probabilistic terms. That is, for each of these possible outcomes, they are associated with a probability, say p1, p2, , pS. For asset i, its expected return is: E(ri) = p1* r1 + p2* r2 + + pS * rS.,An example, I,Portfolio variability measures with ex an
3、te probabilities,The usual variability measure for a portfolio is variance (and standard deviation); holding other factors constant, the lower the variance (and std.), the better. Variance (and std.) measures the degree of possible deviations from the expected return.,Formulas,Var(r) = p1* (E(r) r1)
4、2 + p2* (E(r) r2)2 + + pS * (E(r) rS)2. Std(r) = Var(r)1/2. Variance and standard deviation are non-negative. Standard deviation has the unit as the original data, whereas variance is just a number (has no unit). For this reason, practitioners prefer using standard deviation.,An example, II,2-asset
5、diversification, I,Suppose that you own $100 worth of IBM shares. You remember someone told you that diversification is beneficial. You are thinking about selling 50% of your IBM shares and diversifying into one of the following two stocks: H1 or H2. H1 and H2 have the same expected rate of return a
6、nd variance (std.).,Portfolio return,Portfolio weight for asset i, wi, is the ratio of market value of i to the market value of the portfolio. The return of a portfolio is the weighted (by portfolio weights) average of returns of individual assets. The expected return of a portfolio is the weighted
7、(by portfolio weights) average of expected returns of individual assets.,2-asset diversification, II,2-asset diversification, III,H1 and H2 have the same expected return and variance (std.). Why adding H2 is better than adding H1? The answer is: correlation coefficient. The correlation coefficient b
8、etween IBM and and H2 is lower than that between IBM and H1. That is, with respect to IBMs return behavior, the return behavior of H2 is more unique than that of H1. Return uniqueness is good!,Correlation coefficient,Correlation coefficient measures the mutual dependence of two random returns. Corre
9、lation coefficient ranges from +1 (perfectly positively correlated) to -1 (perfectly negatively correlated). Cov(IBM,H1) = p1* (E(rIBM) rIBM, 1) * (E(rH1) rH1, 1) + p2* (E(rIBM) rIBM, 2) * (E(rH1) rH1, 2) + + pS * (E(rIBM) rIBM, S) * (E(rH1) rH1, S). IBM, H1 = Cov(IBM,H1) / (Std(IBM) * Std(H1) ).,2-
10、asset diversification, IV, = 1, = -1,2-asset diversification,So, these are what we have so far:,The shape of the combinations of 2 assets is like a rubber band. With a low correlation coefficient, you can pull the rubber band further to the left, which is good. Holding other factors constant, the lo
11、wer the correlation coefficient, the better. Again, return uniqueness is healthy!,2-asset formulas,It turns out that there are nice formulas for calculating the expected return and standard deviation of a 2-asset portfolio. Let the portfolio weight of asset 1 be w. The portfolio weight of asset 2 is
12、 thus (1 w). E(r) = w * E(r1) + (1 w) * E(r2). Std(r) = (w2 * Var(r1) + 2* w * (1 w) * cov(1,2) + (1 w)2 * Var(r2)1/2.,Now, let us work on = 0, i.e., Cov=0,N risky assets,The section of the opportunity set above the minimum variance portfolio is the efficient frontier.,return,P,minimum variance port
13、folio,efficient frontier,Individual Assets,N-asset + Rf diversification,Selecting an optimal portfolio from N2 assets,The upper part of the bullet-shape solid line is the efficient frontier (EF): the set of portfolios that have the highest expected return given a particular level of risk (std.). Giv
14、en the EF, selecting an optimal portfolio for an investor who are allowed to invest in a combination of N risky assets is rather straightforward. One way is to ask the investor about the comfortable level of standard deviation (risk tolerance), say 20%. Then, corresponding to that level of std., we
15、find the optimal portfolio on the EF, say the portfolio E shown in the previous figure. CAL (capital allocation line): the set of feasible expected return and standard deviation pairs of all portfolios resulting from combining the risk-free asset and a risky portfolio.,What if one can invest in the
16、risk-free asset?,If we add the risk-free asset to N risky assets, we can enhance the efficient frontier (EF) to the red line shown in the previous figure, i.e., the straight line that passes through the risk-free asset and the tangent point of the efficient frontier (EF). Let us called this straight
17、 line “enhanced efficient frontier” (EEF).,Enhanced efficient frontier (EEF),With the risk-free asset, EEF will be of interest to rational investors who do not like standard deviation and like expected return. Why EEF pass through the tangent point? The reason is that this line has the highest slope
18、; that is, given one unit of std. (variance), the associated expected return is the highest. Why EEF is a straight line? This is because the risk-free asset, by definition, has zero variance (std.) and zero covariance with any risky asset.,Separation, I,When the risk-free asset is available, any eff
19、icient portfolio (any point on the EEF) can be expressed as a combination of the tangent portfolio and the risk-free asset. Implication: in terms of choosing risky investments, there will be no need for anyone to purchase individual stocks separately or to purchase other risky portfolios; the tangen
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