Correlation breakdown? Let’s listen to the data

At certain junctures in each investment cycle, market observers like us note phrases such as "correlations are breaking down". Such an event is crucial, as the structure of our multi-asset portfolios hinges on the intricate concept of diversification. Ideally, correlations should be concentrated enough to avoid being muddled by investment noise, yet not so concentrated as to forfeit the benefits of cross-asset and intra-asset class diversification. 

Recent talk of a correlation breakdown prompted us to explore this issue through an econometric lens. In this weekly edition of Simply put, we assess the current state of correlations using the eigenvalue and clustering methodologies to let the data speak for themselves. 

Of eigenvalues and diversification

When analysing whether correlations are fluctuating, a primary approach involves examining the eigenvalues of the correlation matrix for asset returns. Though the term ‘eigenvalue’ may sound complex, the concept is closely tied to principal component analysis (PCA), a technique used in data analysis.

Eigenvalues in a correlation matrix serve as measures of information pertaining to each joint factor to explain the cross-section of returns. For instance, if the market factor predominantly explains the variation in returns, then the eigenvalue associated with this factor will significantly overshadow the others. These information measures indicate that the higher the eigenvalue, the more information is contained in the corresponding factor.

Figure 1 illustrates the explanatory power of the first few factors (typically one to three, depending on the asset class) for an array of assets, categorised by asset class and then aggregated. Over the long term, and relative to current conditions, it is evident that correlations are indeed breaking down.

Typically, the first few cross-asset factors account for 95% of all returns. However, this number has recently dropped to 86%, with a slight uptick during the brief market correction. Where is this breakdown most apparent? The chart on the right provides the answer: primarily in the equity space. Different regions and sectors demonstrate how investors' focus has recently shifted from the market factor to other areas – which are as yet unknown.

Meanwhile, fixed-income markets continue to experience stress over duration concerns, while credit spreads have remained stable for 18 months, resulting in a market of one for different reasons.

FIG 1. Fraction of market correlations explained by the most significant market factors

Source: Bloomberg, LOIM at May 2024. For illustrative purposes only.

From two to 12 clusters

Another perspective about fluctuating correlations involves the use of clustering methods. Econometrics and statistics provide an array of such methods, all designed with the common goal of grouping similar objects based on a defined measure of distance. This allows users to analyse how these similarities evolve over time.

The connection to the previously discussed approach using eigenvalues is clear: when the first few market factors explain a significant portion of the dynamics of asset returns, large clusters can form. Conversely, when the first few eigenvalues show a lower explanatory power for these common market factors, the number of clusters increases as assets begin to display less resemblance to each other.

Figure 2 presents a heatmap that shows the level of correlation across various assets along with the corresponding clusters, such that more highly correlated assets are placed close to each other while less-correlated assets are far apart. Over the long term, similar to what is shown in figure 1, the number of clusters is typically limited – mostly to two main types: risk assets and hedging assets.

However, the more recent data in the second chart of figure 2 reveals a dramatic change: the number of clusters has significantly increased. Notably, distinct clusters are now forming around specific assets such as the Hang Seng Index, Japanese bonds or the energy equity sector. This sheds further light on the factors that explain the earlier fluctuations observed in the eigenvalues. The current number of clusters found in the analysis of correlation matrices has increased from two to 12 – representing a significant shift.

FIG 2. Cross asset correlations heatmap – 2000-2024 vs 2024

Source: Bloomberg, LOIM. For illustrative purposes only. As at May 2024.

Achieving our portfolio objective of diversification in the current environment is not straightforward due to this breakdown of correlations. Instead of relying on long-term correlations, we believe that understanding and diversifying among risk premia provides a superior way for multi-asset portfolios achieve consistent exposure to different sources of return. This risk-based approach informs our investments across asset classes – and any adaptive of dynamic positioning required.

Macro/nowcasting corner

The most recent evolution of our proprietary nowcasting indicators for global growth, global inflation surprises and global monetary policy surprises are designed to track the recent progression of macroeconomic factors driving the markets.

Our nowcasting indicators currently show:

•    Our growth indicator is on an upward trajectory, but in the US and China indicators are falling slightly
•    In the US and the eurozone, the inflation nowcaster remains high and continues to rise
•    Our monetary policy indicator rose sharply this week, approaching the 50% threshold in the eurozone and the US, mainly due to increased US consumption and eurozone employment data

World growth nowcaster: long-term (left) and recent evolution (right)

World inflation nowcaster: long-term (left) and recent evolution (right)

World monetary policy nowcaster: long-term (left) and recent evolution (right)

Reading note: LOIM’s nowcasting indicator gather economic indicators in a point-in-time manner in order to measure the likelihood of a given macro risk – growth, inflation surprises and monetary policy surprises. The nowcaster varies between 0% (low growth, low inflation surprises and dovish monetary policy) and 100% (the high growth, high inflation surprises and hawkish monetary policy).

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