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?