Recession Probability Remains Unchanged

US Treasury yields ticked lower on Friday after the Federal Reserve’s (Fed) preferred measure of inflation, core PCE, increased +0.3% MoM as expected. This also came after Thursday’s US Q3 GDP release came in stronger than expected at +4.9% QoQ, against +4.5% consensus. Looking forward, Dominique believes that growth will remain above trend. As a result, she believes that Fed guidance at this week’s policy meeting is likely to be more hawkish than the market expects and continues to expect a 2023 hike and quantitative tightening to continue at pace in 2024. 

Turning to market moves, US 10Y yields closed the week at 4.84% (-9bps WoW, +28bps MoM). Meanwhile, the yield on the policy-sensitive US 2Y closed the week at 4.99% (-8bps WoW, -5bps MoM). In terms of yield curve inversion, the magnitude of the 2s10s inversion sat at -15bps 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 the 2Y10Y part of the yield curve, stayed the same as a week earlier at 66% (Chart 1). Meanwhile, the Fed’s recession model, which uses the 3M10Y part of the yield curve, produced a 40% 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.

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