Long-Term Government Bond Yields

Interpretation

The charts above show the 10-year government bond yields of countries across the world, grouped by region. Bond yields reflect the interest rate a government must pay investors to borrow money over the long term, and they are a key barometer of sovereign credit risk, inflation expectations, and monetary policy. Rising yields typically signal higher expected inflation or growth — or increased perceived risk — while falling yields signal the opposite.

Because yields within a monetary union tend to move together, clusters like the Eurozone often trade in tight bands, while emerging markets exhibit wider dispersion driven by local currency, fiscal, and political conditions. Comparing yields across regions helps investors gauge relative value and diversification opportunities across sovereign debt markets.

Interpretation

This heatmap illustrates how the long-term government bond yields of different countries move in relation to one another. Red squares indicate a positive correlation (yields moving together), while blue squares show a negative correlation. Notice the strong clusters among Eurozone countries, reflecting their shared monetary policy and economic ties.

For fixed-income investors, understanding these correlations is essential for geographic diversification. Holding bonds from countries with low or negative yield correlations can help stabilize portfolio returns, especially during periods of economic uncertainty. This strategy aligns with Ray Dalio's principle of using uncorrelated assets to build a more resilient portfolio.

To create this chart, weekly log-changes in bond yields are calculated, and the Pearson correlation is computed for every pair. The All view uses full available history; the 1Y view uses the last 365 days. The heatmap is then organized using hierarchical clustering to group countries with the most similar yield movements, making global economic trends easier to identify.

Interpretation

The Minimum Spanning Tree (MST) uses the same correlation matrix as the heatmap, converts correlations into distances, and keeps the minimum set of links needed to connect the included series. It simplifies the correlation matrix by showing only the strongest connections between bond yields. If two bond yields are linked, they have a strong positive correlation and tend to move in tandem. This helps identify clusters of related assets and is useful for portfolio diversification.

The tree is constructed by converting the correlations into distances and then finding the set of connections that links all bond yields with the minimum total distance. As noted by Marti, Gautier, et al. (2017), the optimal Markowitz portfolio is often found at the tree's outskirts, and the tree tends to shrink during a financial crisis.

Data Sources

• Northern Europe
  • Federal Reserve Bank of St. Louis: Germany, Netherlands, Belgium, Luxembourg, Ireland
  • Stooq: Austria, Finland
• Southern Europe
  • Federal Reserve Bank of St. Louis: Italy, France, Spain, Greece, Portugal, Slovakia, Slovenia
  • Stooq: Cyprus, Malta
• Non-Euro Western & Northern Europe
  • Federal Reserve Bank of St. Louis: United Kingdom, Switzerland, Sweden, Norway, Denmark
  • Stooq: Iceland
• Other Non-Euro Europe
  • Federal Reserve Bank of St. Louis: Czech Republic, Poland
  • Stooq: Russia, Bulgaria, Hungary, Romania, Serbia
• Asian Developed Economies
  • Federal Reserve Bank of St. Louis: Japan, South Korea
  • Stooq: Hong Kong, Singapore, Taiwan
• Asian Developing Economies
  • Stooq: Indonesia, India, Kazakhstan, Malaysia, Philippines, Pakistan, Thailand, Vietnam
• Americas & Oceania
  • Federal Reserve Bank of St. Louis: United States, Canada, South Africa, Mexico, Australia, New Zealand
  • Stooq: Brazil, Colombia, Peru
• Middle East & Africa
  • Stooq: Israel, Egypt, Kenya, Namibia, Nigeria, Zambia