CAPM Is an Asset Pricing Model, Not a Portfolio Optimization Method
Summary
The document distinguishes mean-variance optimization from the Capital Asset Pricing Model. Mean-variance optimization is a portfolio choice problem: it selects weights to minimize variance subject to a target expected return. CAPM, by contrast, describes expected asset returns in relation to the risk-free rate, market return, and an asset’s beta. Treating CAPM as an optimization that minimizes standard deviation misidentifies what the model represents.
The concepts can still be connected. An investor may use an assumed pricing model, including CAPM, when estimating inputs for mean-variance optimization. Conversely, CAPM can be derived under assumptions about how market participants optimize, such as mean-variance preferences. The excerpt gives the core distinction but does not detail those derivations or their assumptions, and it does not support conclusions about variance versus standard deviation as competing portfolio objectives.
Key ideas
- Mean-variance optimization is an optimization problem for choosing portfolio weights.
- CAPM is an asset pricing model that relates expected excess returns to market beta.
- CAPM should not be defined as minimizing standard deviation under a full-investment constraint.
- An asset pricing model can supply assumptions or inputs for a mean-variance optimization.
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# What is the mathematical difference between Mean-Variance Optimization and CAPM?
# What is the mathematical difference between Mean-Variance Optimization and CAPM?
I have spent some time going through the maths of both Mean-Variance Optimization and CAPM, and I'm trying to pin down the mathematical differences between them. For both, let $p$ be a portfolio consisting of risky assets with returns $\mathbf{r}=( r_1,r_2,\dots,r_m)$ and weights $\mathbf{w} = (w_1,w_2,\dots,w_m)$, along with a risk-free asset with return $r_f$, so that
$$r_p = \mathbf{w}^T\mathbf{r}+(1 - \mathbf{w}^T\mathbf{1}_m)r_f$$
- Mean Variance Optimization:
$$\begin{align} \text{Minimize:}\ \ & \frac{1}{2}\mathbf{w}^T\boldsymbol{\Sigma}\mathbf{w} \\ \text{Subject to: }\ \ & \mathbb{E}[r_p] = \mathbf{w}^T\mathbb{E}[\mathbf{r}]+(1 - \mathbf{w}^T\mathbf{1}_m)r_f \end{align}$$
Source: Mathematics for Finance, MIT (I actually couldn't find them mentioning CAPM, just MVO)
- CAPM:
$$\begin{align} \text{Minimize:}\ \ & \sqrt{\mathbf{w}^T\boldsymbol{\Sigma}\mathbf{w}} \\ \text{Subject to: }\ \ & \mathbb{E}[r_p] = \mathbf{w}^T\mathbb{E}[\mathbf{r}]+(1 - \mathbf{w}^T\mathbf{1}_m)r_f \\ \text{Then:}\ \ & \text{Use the specific case where }\mathbf{w}^T\mathbf{1}_m=1 \end{align}$$
Source: QuantPy video which I believe is based on these lecture notes.
So it seems like MVO is minimizing variance, whereas CAPM minimizes standard deviation (as well as assuming full investment in the market). I have never heard the difference explained this way, so is my understanding correct? What are the consequences of choosing to minimize variance vs minimize standard deviation?
## Answer by Richard Hardy (score 5)
https://quant.stackexchange.com/a/78018
The CAPM is an asset pricing model, while mean-variance optimization is a type of optimization. These are objects from two different categories. When you characterize the CAPM as an optimization problem, you are not spelling out what the CAPM is. Instead of your characterization of the CAPM, I would write $$ \mathrm{E}(R_i)-r_f=\beta_i[\mathrm{E}(R_m)-r_f] $$ where $R_i$ is return on asset $i$ and $R_m$ is return on the market.
You can do mean-variance optimization while assuming a certain asset pricing model (such as the CAPM). Or you could try to derive the CAPM assuming the agents in the market do certain optimization (e.g. mean-variance). But again, an asset pricing model and an optimization problem belong to different categories.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.