Main Content

Create Hierarchical Risk Parity Portfolio

This example shows how to compute a hierarchical risk parity (HRP) portfolio. You can use HRP as a technique for portfolio diversification where the assets are divided and weighted according to a hierarchical tree structure. The weights of the assets within a cluster and between clusters can be assigned in many ways. A few ideas of the ways to allocate the weights are:

  • Compute an inverse variance portfolio within each cluster. Then, allocate weights to each cluster using a value proportional to the inverse of the variance of the cluster's portfolio.

  • Compute a risk parity portfolio within each cluster. Then, use a risk parity allocation strategy to assign each cluster's weights. The risk parity between clusters uses the covariance matrix between the cluster's portfolios. This example focuses on this allocation strategy.

  • Use a bisection approach like the one described in Lopez de Prado [1]. For more information, see the example Asset Allocation - Hierarchical Risk Parity.

Begin by loading the data and looking at the correlation between the assets returns.

% Load data
assetRetn = readmatrix("./retns_assets.txt");
[nSample,nAssets] = size(assetRetn);

% Compute covariance and correlation matrices
Sigma = cov(assetRetn);
C = corrcov(Sigma);

Hierarchical Clustering

Hierarchical clustering is a common clustering technique in machine learning. In the context of asset allocation, a hierarchical clustering algorithm is applied to find the distance or similarity between each pair of assets and group them into a multilevel binary hierarchical tree.

Begin by defining a measure of likeness or distance between the assets. The more correlated two assets are, the closer they should be.

% Compute the correlation distance matrix
distCorr = ((1-C)/2).^0.5;

Use the linkage function to compute the matrix that encodes the hierarchical tree of the assets in the universe. Them use the dendrogram function to visualize the hierarchical structure.

% Compute the linkage
link = linkage(distCorr);
h = dendrogram(link, ColorThreshold='default');
set(h, LineWidth=2);
title('Default Leaf Order');

The covariance or correlation matrix can be rearranged to be very close to a block diagonal matrix using the information obtained from the hierarchical tree. Each block in the diagonal shows the assets that are closely related.

% Sort assets for quasi-diagonalization 
nLeafNodes = size(link,1) + 1;
rootNodeId = 2*nLeafNodes - 1;
sortedIdx = getLeafNodesInGroup(rootNodeId,link);
heatmap(C(sortedIdx,sortedIdx), XData=sortedIdx, YData=sortedIdx);

The plot shows that there are 6 blocks of assets that are closer together. You can divide the assets into 6 clusters using the cluster function.

% Get clusters
T = cluster(link, MaxClust=6);

Hierarchical Risk Parity Algorithm

Given a clustering of the assets, the HRP algorithm presented in this example follows these steps:

  1. Build a risk parity portfolio within each cluster. The hrpPortfolio function in Local Functions computes the HRP portfolio by receiving a vector with the cluster assignment and a covariance matrix Σ. Then, a risk parity portfolio is computed within each cluster by using riskBudgetingPortfolio. The riskBudgetingPortfolio function receives a reduced covariance matrix that only includes the information of the assets within the cluster and it returns the weights of the assets in the cluster


where Σj is a matrix whose entries include the covariance information only for the assets in the jth cluster.

2. Compute each cluster's weight using the covariance between each cluster's portfolio. Now, the riskBudgetingPortfolio function receives a matrix (Γ) that represents the covariance between

the cluster's portfolios (wj).


where Wij=wij if asset i is in cluster j, otherwise Wij=0. In other words W=(w1|...|wK), for K clusters and γ=riskBudgetingPortfolio(Γ).

3. The final asset allocation is given by the cluster's risk parity portfolio (wj) multiplied by each cluster's weight (γj)


% Compute HRP portfolio
wHRP = hrpPortfolio(T,Sigma)
wHRP = 14×1


Compare HRP and Mean-Variance Portfolios

Define a long-only, fully-invested mean-variance Portfolio object. Then, compute the associated minimum variance portfolio.

% Define Portfolio object
p = Portfolio(AssetMean=mean(assetRetn), AssetCovar=Sigma);
p = setDefaultConstraints(p); % long-only, fully-invested portfolio
% Min variance portfolio
wMV = estimateFrontierLimits(p,'min');

Visualize the resulting allocations from these two strategies.

% Create pie chart labels to improve plot reading
labels = 1:nAssets;
labels = string(labels);

% Sort assets following quasi-diagonalization order
labels = labels(sortedIdx);
wMV = wMV(sortedIdx);
wHRP = wHRP(sortedIdx);

% Plot pie charts
% Min variance portfolio
title('Min Variance Portfolio',Position=[0,1.5]);
% HRP portfolio
title('HRP Portfolio',Position=[0,1.5]);

You can see that the HRP portfolio is a much more diversified portfolio as compared to the portfolio obtained using the traditional mean-variance approach. In addition, you can see the following:

  • The assets that were not correlated with others, assets 5 and 6, represent a much larger area of the pie. In fact, the sum of the areas of assets that are in the same cluster (for example, assets 1 and 10 or assets 11, 13, 3 and 14) are close to the individual areas of assets 5 and 6. This shows that the weights are divided somewhat evenly between clusters and that each cluster's weight is divided somewhat evenly among the assets within the cluster. This is consistent with the HRP theory.

  • The risk parity portfolio of fully correlated assets with the same variance is an equally weighted portfolio. The same happens for assets that are completely uncorrelated. Since all the assets in the universe have a similar variance, and assets between clusters are almost uncorrelated, the weights allocated to each cluster are almost even. And, since the assets within a cluster are meant to be highly correlated, the weights of the assets within a cluster are evenly distributed.


  1. Lopez de Prado, M. "Building Diversified Portfolios That Outperform Out of Sample." The Journal of Portfolio Management. 42(4), 59-69, 2016.

Local Functions

function pwgt = hrpPortfolio(T,Sigma)
% Function that computes a hierarchical risk parity portfolio. The
% algorithm first computes a risk parity portfolio for each cluster. Then,
% each cluster is assigned a weight based on a risk parity allocation of
% the covariance between the cluster's portfolios.

% Get the problem information.
nAssets = size(Sigma,1);
nClusters = max(T);

% Compute the risk parity portfolio within each cluster.
W = zeros(nAssets,nClusters);
for i = 1:nClusters
    % Identify assets in cluster i and the sub-covariance matrix.
    idx = T == i;
    tempSigma = Sigma(idx,idx);
    % Compute the risk parity portfolio of cluster i.
    W(idx,i) = riskBudgetingPortfolio(tempSigma);

% Compute the covariance between the risk parity portfolios of each
% cluster.
covCluster = W'*Sigma*W;

% Compute the weights of each cluster.
wBetween = riskBudgetingPortfolio(covCluster);

% Multiply the weight assigned to each cluster with its portfolio and
% assign to the corresponding assets.
pwgt = W*wBetween;


function idxInGroup = getLeafNodesInGroup(nodeId, link)
% getLeafNodesInGroup finds all leaf nodes for a given node id
% in a linkage matrix.

nLeaves= size(link, 1)+1;
if nodeId > nLeaves
    tempNodeIds = link(nodeId-nLeaves,1:2);
    idxInGroup = [getLeafNodesInGroup(tempNodeIds(1), link), ...
        getLeafNodesInGroup(tempNodeIds(2), link)];
    idxInGroup = nodeId;

See Also


Related Topics

External Websites