Note

The following implementation and documentation is based on the works of Daniel Lewandowski, Dorota Kurowicka, and Harry Joe: Generating random correlation matrices based on vines and extended onion method and Generating random correlation matrices based partial correlations.

Vine and Extended Onion Methods



Note

Underlying Literature

The following sources elaborate extensively on the topic:

Vines

A vine is a graphical tool for modeling dependence structures of high-dimensional probability distributions. Copulas are multivariate distributions with uniform univariate margins. It examines the association or dependence between many variables.

Vines have been proven to be useful and flexible tools in high-dimensional correlation structure modeling. They consist of a nested set of connected trees where the edges in one tree are the nodes of another tree, and the edges of that tree are the nodes of a third tree and so on.

According to Zhu and Welsch (2018), they are defined as:

\(V\) is an R-vine on \(p\) elements with \(E(V) = E_1 \cup ... \cup E_{p-1}\) denoting a set of edges of \(V\) if:

  • \(V = {T_1, ..., T_{p-1}}\)

  • \(T_1\) is a connected tree with nodes \(N_1 = {1, ..., p}\), and edges \(E_1\)

  • For \(i = 2, ..., p-1\); \(T_i\) is a tree with nodes \(N_i = E_{i-1}\)

Vine Example

Graphical representation of a vine (courtesy of Zhu and Welsch (2018)).

The most common vine structures are regular vines (R-vines,) canonical vine (C-vines,) and drawable vines (D-vines). The following examples are based on the work of to Zhu and Welsch (2018).

R-Vines

A vine is considered a regular vine on \(p\) elements with \(E(V) = E_1 \cup ... \cup E_{p-1}\) denoting the set of edges of \(V\) if:

  • \(V\) is a vine

  • For \(i = 2, ..., p-1\); \({a, b} \in E_i\); \(\#(a \Delta b) = 2\). Where \(\Delta\) denotes the symmetric difference operator, and \(\#\) denotes the cardinality of a set. Therefore, \(a \Delta b = (a \cup b) \setminus (a \cap b)\)

Vine Example

Graphical representation of an R-vine (courtesy of Zhu and Welsch (2018)).

C-Vines

An R-vine is considered a C-vine if each tree \(T_i\) has a unique node of degree \(p-i\) for \(i = 1, ..., p-2\).

Vine Example

Graphical representation of an C-vine (courtesy of Zhu and Welsch (2018)).

D-Vines

An R-vine is considered a D-vine if each node in \(T_1\) has a degree of at most 2.

Vine Example

Graphical representation of an D-vine (courtesy of Zhu and Welsch (2018)).

Partial Correlation Vines

Partial correlation measures the degree of association between two random variables while controlling for a third random variable. It is used to find the variance between two variables while eliminating the variance from the third variable.

A partial correlation vine can be formed with an R-vine, C-vine, or D-vine structure \(V\). Each edge of the vine is identified with a partial correlation coefficient. Thus each edge in \(V\) has can have a value in [-1, 1]. \(V\) specifies \({d \choose 2}\) standard and partial correlations.

Correlation Matrices Generation

In his work, Joe shows that by using a D-vine, we can generate a \(d\)-dimensional random positive definite correlation matrix \(R = (\rho_{i, j})\) by choosing independent distributions \(F_{i, j}, 1 \leq i < j \leq d\), for these \({d \choose 2}\) parameters. \(F_{i, j}\) conditions are chosen so that the joint density of \((\rho_{i, j})\) is proportional to a power of \(det(R)\).

Joe goes on to prove one can generate \(\frac{d \cdot (d-1)}{2}\) partial correlations by independently sampling from a beta distribution of different densities, transformed to the range [-1, 1] and then convert them into raw correlations via a recursive formula. The resulting matrix will be distributed uniformly. The partial correlations can be generated using the D-vine or C-vine methods.

When using the D-vine, the beta distribution \(Beta(\alpha_i, \alpha_i)\) is sampled for \(\alpha_1 = d/2, ..., \alpha_{d-2}, \alpha_{d-1}\) Where

\(\alpha_{d-2} = \alpha_{d-3} - 1/2\)

\(\alpha_{d-1} = \alpha_{d-2} - 1/2\)

Lewandowski, Kurowicka, and Joe extended this method for the C-vine. They introduce the paramater \(\eta\) to sample from a distribution proportional to \(det(R)^{\eta-1}\). The resulting beta distribution \(Beta(\alpha_i, \alpha_i)\) is sampled for \(\alpha_1 = \eta+ (d-1)/2, ..., \alpha_{d-2}, \alpha_{d-1}\) Where

\(\alpha_{d-2} = \alpha_{d-3} - 1/2\)

\(\alpha_{d-1} = \alpha_{d-2} - 1/2\)

Implementation

sample_from_cvine(dim=10, eta=2, n_samples=1, beta_dist_fixed=None)

Generates uniform correlation matrices using the C-vine method.

It is reproduced with modifications from the following paper: Lewandowski, D., Kurowicka, D. and Joe, H., 2009. Generating random correlation matrices based on vines and extended onion method. Journal of multivariate analysis, 100(9), pp.1989-2001.

It uses the partial correlation C-vine to generate partial correlations. The partial correlations are sampled from a uniform beta distribution proportional to its determinant and the factor eta. and adjusted to thr range [-1, 1]. Then these partial correlations are converted into raw correlations by using a recursive formula based on its location on the vine.

Parameters:

  • dim – (int) Dimension of correlation matrix to generate.

  • eta – (int) Corresponds to uniform distribution of beta. Correlation matrix S has a distribution proportional to [det C]^(eta - 1)

  • n_samples – (int) Number of samples to generate.

  • beta_dist_fixed – (tuple) Overrides the beta distribution parameters. The input is two float parameters (alpha, beta), used in the distribution. (None by default)

Returns:

(np.array) Generated correlation matrices of shape (n_samples, dim, dim).

sample_from_dvine(dim=10, n_samples=1, beta_dist_fixed=None)

Generates uniform correlation matrices using the D-vine method.

It is reproduced with modifications from the following paper: Joe, H., 2006. Generating random correlation matrices based on partial correlations. Journal of Multivariate Analysis, 97(10), pp.2177-2189.

It uses the partial correlation D-vine to generate partial correlations. The partial correlations are sampled from a uniform beta distribution and adjusted to thr range [-1, 1]. Then these partial correlations are converted into raw correlations by using a recursive formula based on its location on the vine.

Parameters:

  • dim – (int) Dimension of correlation matrix to generate.

  • n_samples – (int) Number of samples to generate.

  • beta_dist_fixed – (tuple) Overrides the beta distribution parameters. The input is two float parameters (alpha, beta), used in the distribution. (None by default)

Returns:

(np.array) Generated correlation matrices of shape (n_samples, dim, dim).

The Onion Method

The onion method as described by Ghosh and Henderson (2003) is a method to sample exactly and quickly from a uniform distribution. It iteratively creates a correlation matrix by starting with a one-dimensional matrix. Then it ‘grows out’ the matrix by adding one dimension at a time. Lewandowski, Kurowicka, and Joe modified this method and named it the extended onion method. The extend onion samples correlation matrices from a distribution proportional to \(det(R)^{\eta-1}\) as described above. It samples exactly from the uniform distribution over the set of correlation matrices on the surface of a k-dimensional hypersphere, a subset of \(R^{\frac{d-1}{1}}\).

The onion method is based on the fact that any correlation matrix of size \((k+1) \times (k+1)\) can be partitioned as

\[\begin{split}\textbf{r}_{k+1} = \begin{bmatrix} \textbf{r}_{k} & \textbf{z} \\ \textbf{z}^T & 1 \end{bmatrix}\end{split}\]

Where \(\textbf{r}_k\) is an \(k \times k\) correlation matrix and \(\textbf{z}\) is a \(k\)-vector of correlations.

Implementation

sample_from_ext_onion(dim=10, eta=2, n_samples=1)

Generates uniform correlation matrices using extended onion method.

It is reproduced with modifications from the following paper: Lewandowski, D., Kurowicka, D. and Joe, H., 2009. Generating random correlation matrices based on vines and extended onion method. Journal of multivariate analysis, 100(9), pp.1989-2001.

It uses the extended onion to generate correlations sampled from a uniform beta distribution. It starts with a one-dimensional matrix, and it iteratively grows the matrix by adding extra rows and columns by sampling from the convex, closed, compact and full-dimensional set on the surface of a k-dimensional hypersphere.

Parameters:

  • dim – (int) Dimension of correlation matrix to generate.

  • eta – (int) Corresponds to uniform distribution of beta. Correlation matrix S has a distribution proportional to [det C]^(eta - 1)

  • n_samples – (int) Number of samples to generate.

Returns:

(np.array) Generated correlation matrices of shape (n_samples, dim, dim).

Example

Here we show the correlation matrix generated for each method.

Generated Matrices

Plot of a sample generated correlation matrix using each method. 

import matplotlib.pyplot as plt

from mlfinlab.data_generation.vines import (sample_from_cvine,
                                            sample_from_dvine, sample_from_ext_onion)

# Sample a correlation matrix from each method. Matrix has shape (n_samples, dim, dim)
# For this example it corresponds to (1, 100, 100)
dim = 100
n_samples = 1
fig, axes = plt.subplots(3, figsize=(3.5, 7.8))
for i, func in enumerate([sample_from_cvine, sample_from_dvine, sample_from_ext_onion]):
    # Only the D-vine method does not support the eta variable.
    if func == sample_from_dvine:
        corr_mats = func(dim=dim, n_samples=n_samples)
    else:
        corr_mats = func(dim=dim, eta=2, n_samples=n_samples)

    # Plots the correlation matrices generated in a pseudocolor plot
    a = axes[i].pcolormesh(corr_mats[0][:, :], cmap='viridis')
    fig.colorbar(a, ax=axes[i])
    if i == 0:
        title = "C-Vine"
    elif i == 1:
        title = "D-Vine"
    else:
        title = "Extended Onion"
    axes[i].set_title(title)

plt.show()

We show that by biasing the beta distribution from which the C-vine and D-vine methods sample their correlations, we can generate stronger correlations matrices. For a beta distribution \(Beta(\alpha, \beta)\), if \(\alpha \gg \beta\), the resulting correlation matrix has stronger positive correlations. If \(\alpha \ll \beta\), the resulting correlation matrix has stronger negative correlations.

Generated Matrices

Plot of a sample generated correlation matrix using skewed beta distributions for stronger correlations. 

import matplotlib.pyplot as plt

from mlfinlab.data_generation.vines import (sample_from_cvine,
                                            sample_from_dvine, sample_from_ext_onion)

# Sample a correlation matrix from C-vine and D-vine methods, setting the beta parameters
# manually
dim = 100
n_samples = 1
beta_params = [(30, 4), (4, 30)]
title_suffix = ["Positive", "Negative"]
plt.figure(figsize=(8, 6))
i = 0
for j, beta_param in enumerate(beta_params):
    # Generate matrices.
    dvine_mat = sample_from_dvine(dim=dim, n_samples=1, beta_dist_fixed=beta_param)
    cvine_mat = sample_from_cvine(dim=dim, n_samples=1, beta_dist_fixed=beta_param)

    # Plot D-vine.
    fig = plt.subplot(2, 2, i + j + 1)
    fig.set_title("D-vine Strong \n {} Correlations".format(title_suffix[i]))
    plt.pcolormesh(dvine_mat[0][:, :], cmap='viridis')
    plt.colorbar()

    # Plot C-vine.
    fig = plt.subplot(2, 2, j + i + 2)
    fig.set_title("C-vine Strong \n {} Correlations".format(title_suffix[i]))
    plt.pcolormesh(cvine_mat[0][:, :], cmap='viridis')
    plt.colorbar()

    i += 1

plt.show()

Research Notebook

The following research notebook can be used to better understand the Vines and Extended Onion methods.

  • Generating Correlation Matrices using the Vines and Extended Onion Methods

Presentation Slides


References