Goal: I'm trying to frame target volatility investments given some view on what asset to overweight. For example, starting with a risk-parity allocation, tweak the marginal risk contribution of each asset (all equals in risk-parity) and derive the corresponding target weights of the portfolio.

Example: you want to invest in 3 assets. You are given the risk budget for each stock [0.5, 0.3, 0.2]. The goal is to derive the portfolio weights such that the marginal contribution of each asset to the variance of the portfolio is explained by respectively 50%, 30% and 20% for each asset. The goal is to find the portfolio weights $\mathbf{w} \in \mathbb{R}^3$ such that the target vol of the portfolio is $v \in \mathbb{R}^+$.

Question: what the relevant literature on portfolio construction from risk budgeting? A necessary condition for the answer is to provide references. Some python code which solves the example above for a given correlation matrix would be appreciated.

  • $\begingroup$ See the paper by Roncalli Managing Risk Exposures using the Risk Budgeting Approach. With only 3 stocks you don't even need Python, you can do it in Excel in 15 minutes from start to finish. $\endgroup$
    – Alex C
    Jul 2, 2019 at 15:05
  • $\begingroup$ See also thie answers to this quant.stackexchange.com/questions/3114/… $\endgroup$
    – Alex C
    Jul 2, 2019 at 15:34

2 Answers 2


What you want is to design a risk budgeting portfolio. If your constraints are only $\mathbf{1}^T\mathbf{w}=1$ and $\mathbf{w} \geq \mathbf{0}$, then the correct way to do it is to use the formulation proposed by Spinu [1]: $$\begin{array}{ll} \underset{\mathbf{w}}{\textsf{minimize}} & \frac{1}{2}\mathbf{w}^{T}\Sigma\mathbf{w} - \sum_{i=1}^{N}b_i\log(w_i)\\ \textsf{subject to} & \mathbf{1}^T\mathbf{w}=1. \end{array}$$ where $\mathbf{w}$ is the vector of portfolio weights, $\Sigma$ is the covariance matrix, and $b_i, i = 1, 2, ..., N,$ are the risk budgets.

I've implemented a solver for that optimization problem in both R and Python. The code is open source, you can check out the documentations at: https://github.com/dppalomar/riskParityPortfolio (R version) and https://github.com/dppalomar/riskparity.py (Python version).

As a code snippet, you can do it in one line of Python code:

import riskparityportfolio as rp
optimum_weights = rp.vanilla.design(cov, np.array([0.5, 0.3, 0.2]))

where cov is the covariance matrix of your assets.

[1] Florin Spinu, An Algorithm for Computing Risk Parity Weights, 2013. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2297383


PyFENG package implements an improved cyclical coordinate descent (CCD) method based on Choi & Chen (2022):

Below is a 5 asset example. After installing PyFENG (pip install pyfeng), run

import numpy as np
import pyfeng as pf
cov = np.array([
        [ 94.868, 33.750, 12.325, -1.178, 8.778 ],
        [ 33.750, 445.642, 98.955, -7.901, 84.954 ],
        [ 12.325, 98.955, 117.265, 0.503, 45.184 ],
        [ -1.178, -7.901, 0.503, 5.460, 1.057 ],
        [ 8.778, 84.954, 45.184, 1.057, 34.126 ]

m = pf.RiskParity(cov=cov, budget=[0.1, 0.1, 0.2, 0.3, 0.3])


array([0.077, 0.025, 0.074, 0.648, 0.176])

See the PyFENG documentation for more options.


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