In the previous recipe, Finding the Efficient Frontier using optimization with scipy, we found the Efficient Frontier, using numerical optimization with scipy. We used the portfolio volatility as the metric we wanted to minimize. However, it is also possible to state the same problem a bit differently and use convex optimization to find the Efficient Frontier.
We can reframe the mean-variance optimization problem into a risk-aversion framework, in which the investor wants to maximize the risk-adjusted return:
Here, γ ∈ [0, ∞) is the risk-aversion parameter, and the constraints specify that the weights must sum up to 1, and short-selling is not allowed. The higher...
