Monetary Policy & Inflation | US

**US treasury yields rose on Friday** after a mixed jobs report for August. US nonfarm payrolls (NFP) were roughly in line with expectations, unemployment rose 30bp due to an increase in participation, and nominal wage growth slowed MoM but was roughly unchanged YoY.

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**US treasury yields rose on Friday** after a mixed jobs report for August. US nonfarm payrolls (NFP) were roughly in line with expectations, unemployment rose 30bp due to an increase in participation, and nominal wage growth slowed MoM but was roughly unchanged YoY. Contrary to market pricing, Dominique believes the August payrolls are unlikely to change the Fed’s plans for one more hike in 2024, which she expects at the November FOMC meeting.

**Turning to market moves, **US 10Y yields closed the week at 4.18% (-7bps WoW, +10bps MoM) while the yield on the policy-sensitive US 2Y closed the week at 4.87% (-16bps WoW, -1bps MoM). In terms of yield curve inversion, the magnitude of the 2s10s inversion sat at -69bps on Friday, up from year lows of -109bps seen in early July. The probability of recession increases with yield curve inversion.

**The probability of recession within the next twelve months,** assigned by our recession model, which uses the 2Y10Y part of the yield curve, closed the week at 87% (Chart 1). Meanwhile, the Fed’s recession model, which uses the 3M10Y part of the yield curve, produced a 59% chance of recession (Chart 2).

## Background to Models

We introduced two models for predicting US recessions using the slope of the US yield curve. When long-term yields start to fall towards or below short-term yields, the curve flattens or inverts. This has often predicted a recession in subsequent months. Our model is based on the 2s10s curve compared to a model from the Fed that is based on 3M10Y curve. We believe that the 2Y better captures expectations for Fed hikes in coming years and is therefore more forward-looking.

Dalvir Mandara is a Quantitative Researcher at Macro Hive. Dalvir has a BSc Mathematics and Computer Science and an MSc Mathematical Finance both from the University of Birmingham. His areas of interest are in the applications of machine learning, deep learning and alternative data for predictive modelling of financial markets.

Dalvir Mandara is a Quantitative Researcher at Macro Hive. Dalvir has a BSc Mathematics and Computer Science and an MSc Mathematical Finance both from the University of Birmingham. His areas of interest are in the applications of machine learning, deep learning and alternative data for predictive modelling of financial markets.