Kevin Crotty
BUSI 448: Investments
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Today:
\[E[r_i - r_f] = \beta_i \cdot E[r_m-r_f]\]
Empirically, we estimate a market model regression:
\[ r_{i,t} - r_{f,t} = \alpha_i + \beta_i (r_{m,t} - r_{f,t}) + \varepsilon_{i,t} \]
Recall that the tangency portfolio in a frictionless setting satisfies:
\[\begin{align*} \sum_{i=1}^N \text{cov}[r_1,r_i] w_i &= \delta (E[r_1] - r_f) \\ \sum_{i=1}^N \text{cov}[r_2,r_i] w_i &= \delta (E[r_2] - r_f) \\ & \vdots \\ \sum_{i=1}^N \text{cov}[r_N,r_i] w_i &= \delta (E[r_N] - r_f) \end{align*}\] where \(\delta\) is a constant (it is a Lagrange multiplier from the optimization problem)
For asset \(j\):
\[ \sum_{i=1}^N \text{cov}[r_j,r_i] w_i = \delta (E[r_j] - r_f) \]
Rearrange and use the fact that \(r_m = \sum_i w_i r_i\) to get:
\[ E[r_j - r_f] = \delta^{-1} \text{cov}[r_j,r_m] \]
Using the definition of beta:
\[ E[r_j - r_f] = \delta^{-1} \beta_j \text{var}[r_m]\,. \]
Now aggregate this at market weights:
\[ \sum_j w_j \cdot E[r_j - r_f] = \delta^{-1}\text{var}[r_m] \sum_j w_j \cdot \beta_j \]
This implies \(\delta = \text{var}[r_m] / E[r_m - r_f]\), so we arrive at the CAPM formula:
\[ E[r_j - r_f] = \beta_j E[r_m - r_f] . \]
What if this weren’t the case?
Empirically, this is challenging.
An additional complication: the MRP is likely time-varying.
Years of Data | Standard Error of Estimates |
---|---|
5 | 8.94% |
10 | 6.32% |
25 | 4.00% |
50 | 2.83% |
100 | 2.00% |
The security market line is the visual representation of the CAPM and the cross-section of expected returns
The CAPM doesn’t fit realized returns in the cross-section of stocks very well.
Theoretically, the slope of the SML should be:
Empirically, the slope is much flatter than the realized market risk premium.
Let’s look at what this webpage is doing.
BUSI 448