The role of technical factors in euro area inflation‑linked swap rates

Prepared by Asger Munch Grønlund, Kasper Jørgensen and Fabian Schupp

Published as part of the ECB Economic Bulletin, Issue 3/2024.

This box looks at whether euro area inflation-linked swap (ILS) rates purely reflect inflation compensation – or if these also reflect “technical” factors, which would reduce their information content. When long-term ILS rates for the euro area peaked at 2.7% in summer 2023, their underlying drivers and their information content for monetary policy purposes came under scrutiny. Conceptually, ILS rates comprise the genuine inflation expectations of market participants plus inflation risk premia – referred to together as “inflation compensation” – in the absence of market imperfections. According to asset-pricing theory, risk-averse investors demand inflation risk premia as compensation for being exposed to inflation risk, which is typically positive in periods dominated by aggregate supply shocks.^[1] However, owing to market imperfections, ILS rates may not solely reflect inflation compensation as narrowly defined above. For instance, a pension fund that was subject to regulation as regards its exposure to risks might be willing to pay more to hedge inflation risk than one would anticipate on the basis of its inflation expectations and a measure of uncertainty about the inflation outlook. Instrument‑specific liquidity risk premia could also create a wedge between ILS rates and pure inflation compensation. This box uses the catch-all term “technical factors” to cover all additional components arising from market imperfections.

We propose a simple econometric model which quantifies the share of total variation in euro area ILS rates that can be explained by inflation compensation in a narrow sense, thereby quantifying the role played by any technical factors. Our method adds a new econometric perspective and complements existing qualitative and quantitative approaches to identifying technical distortions in market-based measures of inflation compensation.^[2] We do not decompose inflation compensation into genuine inflation expectations and inflation risk premia, instead regarding the two components as a single fundamental factor.^[3]

Our econometric approach is based on the idea that all relevant information about pure inflation compensation (i.e. inflation compensation excluding any technical factors) should be reflected in nominal overnight index swap (OIS) rates. Conceptually, €STR OIS rates should comprise euro area real rates plus inflation compensation. Hence, we test the hypothesis that ILS rates mainly reflect inflation compensation by estimating the variation in ILS rates that can be explained by information inferred from OIS rates. We assume that nominal OIS rates are explained by a factor model: the OIS rate for each maturity is the weighted sum of a small set of (possibly unobservable) economic pricing factors, with each maturity having its own weights. Real OIS rates are assumed to be weighted sums of the same pricing factors, but with different weights. Accordingly, the (unobserved) inflation compensation contained in OIS rates is also, necessarily, a weighted sum of the underlying pricing factors. Suppose that observable ILS rates reflect not only fundamental inflation compensation, but also an ILS-specific technical factor (e.g. a liquidity premium). That additional driver would not form part of the aforementioned fundamental pricing factors. In that case, if we back out pricing factors from OIS rates (using statistical principal component analysis, for example) and regress ILS rates on those factors, any ILS-specific technical factors not covered by OIS rates will show up in the residual.^[4],[5]

The share of total variation that is not explained by this simple econometric model constitutes an upper bound for the role of technical factors. If those technical factors are all absent, theory suggests that all variation should be explained by the fundamental factors, and the regression will thus achieve an R2 of 1.^[6] In practice, even in the absence of technical factors, such an extreme result seems unlikely for several reasons. First, the underlying OIS factors are not directly observable (requiring estimation on the basis of a finite sample). Second, there may be non-linearities in the relationship between OIS rates and the pricing factors (e.g. owing to the effective lower bound on nominal interest rates). Third, ILS rates are affected by an indexation lag, whereas OIS rates, being purely forward-looking, are not. And fourth, the pricing factors recovered from OIS rates may not capture “hidden” factors that have opposing impacts on the inflation compensation and real rate components of OIS rates such that those components offset each other completely.^[7] By implication, this means that if the regression model, despite those reasons, still produces a high value for R2, technical factors are unlikely to be making a meaningful contribution to the dynamics of ILS rates.

Our empirical results suggest that euro area ILS rates mainly reflect pure inflation compensation, with little room for technical factors outside of some short-lived distortions in short-term ILS rates at times of extreme market volatility. Using the model outlined above, we regress ILS rates with maturities of one to ten years on the first three principal components inferred from OIS rates with maturities of between three months and ten years, as well as realised month‑on‑month and year-on-year headline inflation.^[8] Overall, the regression model fits observed ILS rates very well, meaning that the fitted series closely reflect the observed levels and dynamics of ILS rates across maturities (see Charts A and B for one-year forward rates one year ahead and five-year forward rates five years ahead respectively).^[9] This is also reflected in a high average R2 of 0.9 across maturities (see the blue bars in Chart C), with the room for technical factors being slightly larger for the shortest maturities.^[10] While the residuals are small on average, there are a few periods where they deviate more significantly from zero – most notably, the global financial crisis, the start of the COVID-19 pandemic and the aftermath of the Russian invasion of Ukraine. As those were periods of heightened market volatility, it is not unreasonable to think that the signal from market-based measures of inflation compensation could have been distorted somewhat by factors other than inflation expectations and inflation risk premia. However, the deviations are only short-lived, as reflected in the near-zero means for the residuals in each individual year in the sample, while the volatility of the residuals – as captured by their standard deviation – spikes in the years that include those episodes (see Charts A and B).

Chart A

Fit for one-year forward ILS rates one year ahead

(left-hand scale: percentages per annum; right-hand scale: percentage points)

Chart B

Fit for five-year forward ILS rates five years ahead

(left-hand scale: percentages per annum; right-hand scale: percentage points)

Chart C

Variation in euro area ILS rates and break-even inflation rates that can be explained by OIS factors and realised inflation

(adjusted R²)

However, our analytical framework suggests that there is more room for technical factors in euro area BEIRs inferred from inflation-linked bonds. The regressions using BEIRs derived from the bond market generally have a lower R2 (see the yellow bars in Chart C). This is consistent with the commonly held view that BEIRs are more prone to technical factors owing to the relatively low levels of liquidity in the ILB market.^[11] These results suggest that the average R2 of 0.9 achieved for euro area ILS rates is strikingly high and that such high levels of explanatory power are not common. In particular, such high levels are not seen in markets with relatively low levels of liquidity, such as the ILB market.