concentrationIndices

Compute ad-hoc concentration indices for a portfolio

Syntax

ci = concentrationIndices(PortfolioData)

[ci,Lorenz] = concentrationIndices(___,Name,Value)

Description

ci = concentrationIndices(PortfolioData) computes multiple ad-hoc concentration indices for a given portfolio. The concentrationIndices function supports the following indices:

• CR — Concentration ratio

• Deciles — Deciles of the portfolio weights distribution

• Gini — Gini coefficient

• HH — Herfindahl-Hirschman index

• HK — Hannah-Kay index

• HT — Hall-Tideman index

• TE — Theil entropy index

[ci,Lorenz] = concentrationIndices(___,Name,Value) adds optional name-value pair arguments.

Examples

Compute Concentration Indices for a Credit Portfolio

Compute the concentration indices for a credit portfolio using a portfolio that is described by its exposures. The exposures at default are stored in the EAD array.

Load the CreditPortfolioData.mat file that contains EAD used for the PortfolioData input argument.

load CreditPortfolioData.mat
ci = concentrationIndices(EAD)

ci=1×8 table
        ID            CR          Deciles        Gini         HH          HK          HT         TE   
    ___________    ________    _____________    _______    ________    ________    ________    _______

    "Portfolio"    0.058745    [1x11 double]    0.55751    0.023919    0.013363    0.022599    0.53485

Compute Multiple Concentration Ratios

Use the CRIndex optional input to obtain the concentration ratios for the tenth and twentieth largest exposures. In the output, the CR column becomes a vector, with one value for each requested index.

Load the CreditPortfolioData.mat file that contains the EAD used for the PortfolioData input argument.

load CreditPortfolioData.mat
ci = concentrationIndices(EAD,'CRIndex',[10 20])

ci=1×8 table
        ID                 CR               Deciles        Gini         HH          HK          HT         TE   
    ___________    __________________    _____________    _______    ________    ________    ________    _______

    "Portfolio"    0.38942    0.58836    [1x11 double]    0.55751    0.023919    0.013363    0.022599    0.53485

Modify the Alpha Parameter of the Hannah-Kay Index

Use the HKAlpha optional input to set the alpha parameter for the Hannah-Kay (HK) index. Use a vector of alpha values to compute the HK index for multiple parameter values. In the output, the HK column becomes a vector, with one value for each requested alpha value.

Load the CreditPortfolioData.mat file that contains EAD used for the PortfolioData input argument.

load CreditPortfolioData.mat
ci = concentrationIndices(EAD,'HKAlpha',[0.5 3])

ci=1×8 table
        ID            CR          Deciles        Gini         HH                HK                HT         TE   
    ___________    ________    _____________    _______    ________    ____________________    ________    _______

    "Portfolio"    0.058745    [1x11 double]    0.55751    0.023919    0.013363    0.029344    0.022599    0.53485

Create an ID to Compare Concentration Index Results

Compare the concentration measures using an ID optional argument for a fully diversified portfolio and a fully concentrated portfolio.

ciD = concentrationIndices([1 1 1 1 1],'ID','Fully diversified');
ciC = concentrationIndices([0 0 0 0 5],'ID','Fully concentrated');
disp([ciD;ciC])

             ID             CR        Deciles       Gini    HH     HK     HT         TE     
    ____________________    ___    _____________    ____    ___    ___    ___    ___________

    "Fully diversified"     0.2    [1x11 double]      0     0.2    0.2    0.2    -2.2204e-16
    "Fully concentrated"      1    [1x11 double]    0.8       1      1      1         1.6094

Apply Scaling to Concentration Indices

Use the ScaleIndices optional input to scale the index values of Gini, HH, HK, HT, and TE. The range of ScaleIndices is from 0 through 1, independent of the number of loans.

ciDU = concentrationIndices([1 1 1 1 1],'ID','Diversified, unscaled');
ciDS = concentrationIndices([1 1 1 1 1],'ID','Diversified, scaled','ScaleIndices',true);
ciCU = concentrationIndices([0 0 0 0 5],'ID','Concentrated, unscaled');
ciCS = concentrationIndices([0 0 0 0 5],'ID','Concentrated, scaled','ScaleIndices',true);
disp([ciDU;ciDS;ciCU;ciCS])

               ID               CR        Deciles       Gini        HH            HK             HT             TE     
    ________________________    ___    _____________    ____    __________    ___________    ___________    ___________

    "Diversified, unscaled"     0.2    [1x11 double]      0            0.2            0.2            0.2    -2.2204e-16
    "Diversified, scaled"       0.2    [1x11 double]      0     3.4694e-17    -3.4694e-17    -6.9389e-17    -1.3796e-16
    "Concentrated, unscaled"      1    [1x11 double]    0.8              1              1              1         1.6094
    "Concentrated, scaled"        1    [1x11 double]      1              1              1              1              1

Plot an Approximate Lorenz Curve Using Deciles Information

Load the CreditPortfolioData.mat file that contains EAD used for the PortfolioData input argument.

load CreditPortfolioData.mat
P = EAD;
ci = concentrationIndices(P);

Visualize an approximate Lorenz curve using the deciles information and also the concentration at the decile level.

Proportion = 0:0.1:1;

figure;
subplot(2,1,1)
area(Proportion',[ci.Deciles' Proportion'-ci.Deciles'])
axis([0 1 0 1])
title('Lorenz Curve (By Deciles)')
xlabel('Proportion of Loans')
ylabel('Proportion of Value')

subplot(2,1,2)
bar(diff(ci.Deciles))
axis([0 11 0 1])
title('Concentration by Decile')
xlabel('Decile')
ylabel('Weight')

Plot an Exact Lorenz Curve Using the Optional Lorenz Output

Load the CreditPortfolioData.mat file that contains the EAD used for the PortfolioData input argument. The optional output Lorenz contains the data for the exact Lorenz curve.

load CreditPortfolioData.mat
P = EAD;
[~,Lorenz] = concentrationIndices(P);

figure;
area(Lorenz.ProportionLoans,[Lorenz.ProportionValue Lorenz.ProportionLoans-Lorenz.ProportionValue])
axis([0 1 0 1])
title('Lorenz Curve')
xlabel('Proportion of Loans')
ylabel('Proportion of Value')

Input Arguments

PortfolioData — Nonnegative portfolio positions in N assets

numeric array

Nonnegative portfolio positions in N assets, specified as an N-by-1 (or 1-by-N) numeric array.

Data Types: double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: [ci,Lorenz] = concentrationIndices(PortfolioData,'CRIndex',100)

'CRIndex' — Index of interest for concentration ratio

1 (default) | nonnegative integer

Index of interest for the concentration ratio, specified as the comma-separated pair consisting of 'CRIndex' and an integer value between 1 and N, where N is the number of assets in the portfolio. The default value for CRIndex is 1 (the default CR is the largest portfolio weight). If CRIndex is a vector, the concentration ratio is computed for the index value in the given order.

Data Types: double

'HKAlpha' — Alpha parameter for Hannah-Kay index

0.5 (default) | nonnegative numeric

Alpha parameter for Hannah-Kay index, specified as the comma-separated pair consisting of 'HKAlpha', and a positive number that cannot be equal to 1. If HKAlpha is a vector, the Hannah-Kay index is computed for each alpha value in the given order.

Data Types: double

'ID' — User-defined ID for portfolio

"Portfolio" (default) | character vector | string object

User-defined ID for the portfolio, specified as the comma-separated pair consisting of 'ID' and a scalar string object or character vector.

Data Types: char | string

'ScaleIndices' — Flag to indicate whether to scale concentration indices

false (no scaling) (default) | logical

Flag to indicate whether to scale concentration indices, specified as the comma-separated pair consisting of 'ScaleIndices' and a logical scalar. When the ScaleIndices is set to true, the value of the Gini, HH, HK, HT, and TE indices are scaled so that all these indices have a minimum value of 0 (full diversification) and a maximum value of 1 (full concentration).

Note

Scaling is applied only for portfolios with at least two assets. Otherwise, the scaling capability is undefined.

Data Types: logical

Output Arguments

ci — Concentration indices information for given portfolio

table

Concentration indices information for the given portfolio, returned as a table with the following columns:

• ID — Portfolio ID string. Use the ID name-value pair argument to set it.

• CR — Concentration ratio. By default, the concentration ratio for the first index (largest portfolio weight) is reported. Use the CRIndex name-value pair argument to choose a different index. If CRIndex is a vector of length m, then CR is a row vector of size 1-by-m. For more information, see More About.

• Deciles — Deciles of the portfolio weights distribution is a 1-by-11 row vector containing the values 0, the nine decile cut points, and 1. For more information, see More About.

• Gini — Gini coefficient. For more information, see More About.

• HH — Herfindahl-Hirschman index. For more information, see More About.

• HK — Hannah-Kay index (reciprocal). By default, the 'alpha' parameter is set to 0.5. Use the HKAlpha name-value pair argument to choose a different value. If HKAlpha is a vector of lengthm, then HK is a row vector of size 1-by-m. For more information, see More About.

• HT — Hall-Tideman index. For more information, see More About.

• TE — Theil entropy index. For more information, see More About.

Lorenz — Lorenz curve data

table

Lorenz curve data, returned as a table with the following columns:

• ProportionLoans — (N+1)-by-1 numeric array containing the values 0, 1/N, 2/N, ... N/N = 1. This is the data for the horizontal axis of the Lorenz curve.

• ProportionValue — (N+1)-by-1 numeric array containing the proportion of portfolio value accumulated up to the corresponding proportion of loans in the ProportionLoans column. This is the data for the vertical axis of the Lorenz curve.

More About

Portfolio Notation

All the concentration indices for concentrationIndices assume a credit portfolio with an exposure to counterparties.

Let P be a given credit portfolio with exposure to N counterparties. Let x₁,...x_N represent the exposures to each counterparty, with x_i > = 0 for all i = 1,...N. And, let x be the total portfolio exposure

$$x={\displaystyle \sum _{i=1}^N{x}_i}$$

Assume that x > 0, that is, at least one exposure is nonzero. The portfolio weights are given by w₁,...,w_N with

$$w_i=\frac{x_i}{x}$$

The weights are sorted in non-decreasing order. The following standard notation uses brackets around the indices to denote ordered values.

$$w_{[1]}\le w_{[2]}\le \mathrm{...}\le w_{[N]}$$

Concentration Ratio

The concentration ratio (CR) answers the question “what proportion of the total exposure is accumulated in the largest k loans?”

The formula for the concentration ratio (CR) is:

$$C{R}_k={\displaystyle \sum _{i=1}^k{w}_{{}_{[N-i+1]}}}$$

For example, if k=1, CR₁ is a sum of the one term w_[N-1+1] = w_[N], that is, it is the largest weight. For any k, the CR index takes values from 0 through 1.

Lorenz Curve

The Lorenz curve is a visualization of the cumulative proportion of portfolio value (or cumulative portfolio weights) against the cumulative proportion of loans.

The cumulative proportion of loans (p) is defined by:

$$p_0=0,p_1=\frac{1}{N},p_2=\frac{2}{N},\mathrm{...},p_N=\frac{N}{N}=1$$

The cumulative proportion of portfolio value L is defined as:

$$L_0=0,L_k={\displaystyle {\sum }_{i=1}^k{w}_{[i]}}$$

The Lorenz curve is a plot of L versus p, or the cumulative proportion of portfolio value versus cumulative proportion of the number of loans (sorted from smallest to largest).

The diagonal line is indicated in the same plot because it represents the curve for the portfolio with the least possible concentration (all loans with the same weight). The area between the diagonal and the Lorenz curve is a visual representation of the Gini coefficient, which is another concentration measure.

Deciles

Deciles are commonly used in the context of income inequality.

If you sort individuals by their income level, what proportion of the total income is earned by the lowest 10% and the lowest 20% of the population? In a credit portfolio, loans can be sorted by exposure. The first decile corresponds to the proportion of the portfolio value that is accumulated by the smallest 10% loans, and so on. Deciles are proportions, therefore they always take values from 0 through 1.

Defining the cumulative proportion of loans (p) and the cumulative proportion of values L as in Lorenz Curve, the deciles are a subset of the proportion of value array. Given indices d1, d2,…, d9 such that the proportion of loans matches exactly these values:

$$p_{d1}=0.1,p_{d2}=0.2,\mathrm{...},p_{d9}=0.9$$

The deciles D₀,D₁,....,D₉,D₁₀ are defined as the corresponding proportion of values:

$$D_0=L_0=0,D_1=L_{d1},D_2=L_{d2},\mathrm{...},D_9=L_{d9,}{D}_{10}=L_N=1$$

When the total number of loans N is not divisible by 10, no indices match the exact proportion of loans 0.1, 0.2, and so on. In that case, the decile values are linearly interpolated from the Lorenz curve data (that is, from the p and L arrays). With this definition, there are 11 values in the deciles information because the end points 0% and 100% are included.

Gini Index

The Gini index (or coefficient) is visualized on a Lorenz curve plot as the area between the diagonal and the Lorenz curve.

Technically, the Gini index is the ratio of that area to the area of the full triangle under the diagonal on the Lorenz curve plot. The Gini index is also defined equivalently as the average absolute difference between all the weights in the portfolio normalized by the average weight.

Using the proportion of values that array L defined in the Lorenz curve section, the Gini index is given by the formula:

$$Gini=1-\frac{1}{N}{\displaystyle {\sum }_{i=1}^N(L_{i-1}+L_i)}$$

Equivalently, the Gini index can be computed from the sorted weights directly with the formula:

$$Gini=\frac{1}{N}{\displaystyle {\sum }_{i=1}^N(2i-1)w_{[i]}-1}$$

The Gini coefficient values are always between 0 (full diversification) and 1– 1/N (full concentration).

Herfindahl-Hirschman Index

The Herfindahl-Hirschman index is commonly used as a measure of market concentration.

The formula for the Herfindahl-Hirschman index is:

$$HH={\displaystyle {\sum }_{i=1}^N{w}_i{}^2}$$

The Herfindahl-Hirschman index takes values between 1/N (full diversification) and 1 (full concentration).

Hannah-Kay Index

The Hannah-Kay index is a generalization of the Herfindahl-Hirschman index.

The formula for the Hannah-Kay depends on a parameter α > 0, α ≠ 1, as follows:

$$H{K}_{\alpha }={\left({\displaystyle {\sum }_{i=1}^N{w}_i{}^{\alpha }}\right)}^{1/(\alpha -1)}$$

This formula is the reciprocal of the original Hannah-Kay index, which is defined with 1/(1− α) in the exponent. For concentration analysis, the reciprocal formula is the standard because it increases as the concentration increases. This is the formula implemented in concentrationIndices. The Hannah-Kay index takes values between 1/N (full diversification) and 1 (full concentration).

Hall-Tideman Index

The Hall-Tideman index is a measure commonly used for market concentration.

The formula for the Hall-Tideman index is:

$$HT=\frac{1}{2{\displaystyle {\sum }_{i=1}^N(N-i+1)w_{[i]}-1}}$$

The Hall-Tideman index takes values between 1/N (full diversification) and 1 (full concentration).

Theil Entropy Index

The Theil entropy index, based on a traditional entropy measure (for example, Shannon entropy), is adjusted so that it increases as concentration increases (entropy moves in the opposite direction), and shifted to make it positive.

The formula for the Theil entropy index is:

$$TE={\displaystyle {\sum }_{i=1}^N{w}_i\log (w_i)+\log (N)}$$

The Theil entropy index takes values between 0 (full diversification) and log(N) (full concentration).

References

[1] Basel Committee on Banking Supervision. "Studies on Credit Risk Concentration". Working paper no. 15. November, 2006.

[2] Calabrese, R., and F. Porro. "Single-name concentration risk in credit portfolios: a comparison of concentration indices." working paper 201214, Geary Institute, University College, Dublin, May, 2012.

[3] Lütkebohmert, E. Concentration Risk in Credit Portfolios. Springer, 2009.