Getting smarter - the case for global GDP weighted sov.bond indexes strengthens?

In recent years, as global debt levels of major economies have increased, and focus on fundamental factors has increased, the case for introducing fundamentally-driven or “ smart “ benchmarks, like GDP-weighted government bond indexes, as an alternative to market value (MV) debt-weighted indexes has strengthened. The Eurozone sovereign debt crisis was a good example of the inherent risks in MV debt-weighted benchmarks, partly due to contagion. Even before the Eurozone debt crisis, Bruder[note2] et al found evidence of outperformance by GDP weighted benchmarks versus MV debt-weighted indexes. 

The difficulty here is that as debt levels rise in MV indexes, the weight of more indebted countries automatically increases (the so-called “ bums problem” in fixed income indexing). So, a passive investor in a bond market index based on the MV of debt alone is not taking a neutral view, since the investor is increasing exposure to more indebted countries as the issuance increases ( or corporates in a corporate bond index). There is also evidence from other asset classes of benchmarks based on MV alone causing global index weightings to be distorted, ie, Japan’s weight in global equity indexes in the late-1980s.

There are a number of factors supporting GDP-weighted indices fundamentally. Firstly, GDP growth and GDP size is an important measure of debt capacity. Thus sustainability metrics like debt levels, or debt service costs are not based on outright levels alone but as ratios of debt to GDP. Ceteris paribus, faster growing economies therefore are less exposed to debt sustainability issues, when the denominator rises faster in the ratio of debt/GDP. This should lower sovereign default risks. In contrast, Chart 1 shows the rapid growth in G7 debt/GDP ratios since the GFC, raising investor debt sustainability concerns.

Secondly, GDP weighting for bond indices recognises the impact of major shifts in the structure of the global economy in recent years, particularly faster relative GDP growth in emerging markets, alongside slower G7 growth. In that regard, we note EM local currency government bond markets have delivered far stronger total returns than G7 markets since Covid, as Chart 2 shows.

Thirdly, using GDP weights increases the diversification of global bond indices, and reduces concentration risk in a smaller number of highly indebted G7 nations. It also dampens the effect of a strong or weak dollar in raising, or reducing the US country weight, and stabilises country weights. The combination of the strong US dollar from 2014-24 and rapid increase in US debt issuance, took the US weight in the WGBI up to 45% at the peak, and it still exceeds 40%.

Building global bond indices based on GDP weights raises a further question – whether to use nominal GDP or Purchasing Power Parity (PPP) GDP weights in global GDP ? Nominal GDP weights use prevailing market exchange rates and GDP levels, and then calculate the share of that country’s GDP in global GDP, in a common numeraire, normally the US dollar. This measure gives a snapshot of the nominal value of a country’s GDP, relative size and ability to import goods. However, nominal GDP makes no allowance for differences in the cost of living in different economies, and is   sensitive to movements in exchange rates. So US dollar appreciation from 2014-24 increased the nominal value of US GDP relative to other economies, and the US weight in global GDP. 

To make allowance for differences in the cost of living, the PPP exchange rate can be used. The PPP exchange rate is the conversion rate between the currency of one country and another that equalises the cost of the same basket of goods in the two countries. A frequently quoted example is the so-called Big-Mac [note3] index, in which the cost of hamburgers is compared in different countries, giving an implied PPP exchange rate between countries for hamburgers. 

To enable broader price comparisons across countries, the International Comparisons Program was implemented by the UN and University of Pennsylvania in 1968, and PPP exchange rates are based on this global price survey.  PPP exchange rates tend to be more stable than market exchange rates over time, and capture the impact of non-tradeable goods and services on economic well-being. Market exchange rates are only relevant for internationally traded goods.

For EM economies, the gap between market and PPP exchange rates is generally much larger than for advanced economies. Indeed, for most EM economies, the ratio of market and PPP US dollar exchange rates is between 2 and 4. So using PPP exchange rates for EM economies boosts their global GDP weights considerably. The OECD uses PPP exchange rates to calculate global GDP weights, while the World Bank and IMF use both PPP and market exchange rates. The natural time lag between the price surveys and publication of the results is an obvious deficiency of PPP exchange rates   and neither methodology is perfect, which explains why the IMF, for example, uses both methodologies. But PPP exchange rates may be a better guide to overall living standards, even if there are measurement issues and they cannot be updated as frequently as market exchange rates.

Turning to the performance and characteristics of global GDP country weights, the FTSE Russell (PPP) GDP country weights, based on IMF data, show EM economy weights are greater on a PPP basis, than their WGBI weights, and particularly China, which has a 33.7% weight. In comparison, (PPP) GDP country weights of DM economies are mainly below WGBI weights, due to higher issuance in those DM countries. The US (PPP) GDP weighting is below that of China, at 25.6%. Table 1 shows these weighting differences.

Source: FTSE Russell, data to January 31, 2026. Past performance is not a guide to future returns. Please see the end for important legal disclosures. 

Extending the GDP-weighting methodology to the Emerging Markets Government Bond index (EMGBI) makes less difference to overall performance, but does increase the country weights of India and Brazil in the index notably, by 12% and 4%, whilst reducing China’s country weight by 13.5%. This causes the GDP-weighted variants of the EMGBI to underperform the EMGBI modestly, as Table 3 shows, since China government debt has outperformed virtually all govt bond markets over the period since 2015, given the combination of deflation risks and the safe haven appeal of government bonds during the property crash.

Source: FTSE Russell, data to end-January 2026. Past performance is not a guide to future returns. Please see the end for important legal disclosures. 

However, GDP-weighting the EMGBI gives a better diversified index by country weight, and reduces concentration risk in China, where yields are now near all-time lows. So the China effect within the EMGBI index from using GDP-weights works the other way from the WGBI-GDP adjusted variants, with China’s weighting falling to 45% from 58% in the EMGBI index. 

Finally, we would point out that both the WGBI and EMGBÍ country-weighted indices are simply examples of how standard, market-value driven indices can be customised, or flexed, for a new global fixed income regime of more indebted sovereigns and faster growing EM nations. These variants have the attraction of protecting investors from concentration risk and the debtor bias in pure market-value indices, and increase the weighting of faster growing economies with more debt service capacity. Methodologically, this seems a good starting point. But these are not the only customisations available – FTSE Russell already has a China-capped EMGBI version which reduces China concentration risk further, and we continue research on factor-tilted variations, to capture structural change in the global economy and fixed income markets.